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%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.00",
%%%     date            = "20 November 2019",
%%%     time            = "08:04:59 MDT",
%%%     filename        = "topc.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "http://www.math.utah.edu/~beebe",
%%%     checksum        = "60586 1538 8847 82983",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "ACM Transactions on Social Computing (TSC);
%%%                        bibliography; BibTeX",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a COMPLETE BibTeX bibliography for
%%%                        ACM Transactions on Social Computing (TSC)
%%%                        (CODEN ????, ISSN 2469-7818 (print),
%%%                        2469-7826 (electronic)).  The journal appears
%%%                        quarterly, and publication began with volume
%%%                        1, number 1, in February 2018.
%%%
%%%                        At version 1.00, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2018 (  19)    2019 (  10)
%%%
%%%                             Article:         29
%%%
%%%                             Total entries:   29
%%%
%%%                        The journal Web page can be found at:
%%%
%%%                            http://tsc.acm.org/
%%%
%%%                        The journal table of contents page is at:
%%%
%%%                            http://dl.acm.org/pub.cfm?id=J1546
%%%                            http://dl.acm.org/citation.cfm?id=2632163
%%%
%%%                        Qualified subscribers can retrieve the full
%%%                        text of recent articles in PDF form.
%%%
%%%                        The initial draft was extracted from the ACM
%%%                        Web pages.
%%%
%%%                        ACM copyrights explicitly permit abstracting
%%%                        with credit, so article abstracts, keywords,
%%%                        and subject classifications have been
%%%                        included in this bibliography wherever
%%%                        available.  Article reviews have been
%%%                        omitted, until their copyright status has
%%%                        been clarified.
%%%
%%%                        URL keys in the bibliography point to
%%%                        World Wide Web locations of additional
%%%                        information about the entry.
%%%
%%%                        BibTeX citation tags are uniformly chosen
%%%                        as name:year:abbrev, where name is the
%%%                        family name of the first author or editor,
%%%                        year is a 4-digit number, and abbrev is a
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%%%
%%%                        In this bibliography, entries are sorted in
%%%                        publication order, using ``bibsort -byvolume.''
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%%% ====================================================================
%%% Acknowledgement abbreviations:
@String{ack-nhfb = "Nelson H. F. Beebe,
                    University of Utah,
                    Department of Mathematics, 110 LCB,
                    155 S 1400 E RM 233,
                    Salt Lake City, UT 84112-0090, USA,
                    Tel: +1 801 581 5254,
                    FAX: +1 801 581 4148,
                    e-mail: \path|beebe@math.utah.edu|,
                            \path|beebe@acm.org|,
                            \path|beebe@computer.org| (Internet),
                    URL: \path|http://www.math.utah.edu/~beebe/|"}

%%% ====================================================================
%%% Journal abbreviations:
@String{j-TSC                   = "ACM Transactions on Social Computing (TSC)"}

%%% ====================================================================
%%% Bibliography entries:
@Article{Crowston:2018:IAT,
  author =       "Kevin Crowston",
  title =        "Introduction to {{\booktitle{ACM Transactions on
                 Social Computing}}}",
  journal =      j-TSC,
  volume =       "1",
  number =       "1",
  pages =        "1:1--1:??",
  month =        feb,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3181713",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:50 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3181713",
  acknowledgement = ack-nhfb,
  articleno =    "1e",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Schmitz:2018:OSN,
  author =       "Heinz Schmitz and Ioanna Lykourentzou",
  title =        "Online Sequencing of Non-Decomposable Macrotasks in
                 Expert Crowdsourcing",
  journal =      j-TSC,
  volume =       "1",
  number =       "1",
  pages =        "1:1--1:??",
  month =        feb,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3140459",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:50 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3140459",
  abstract =     "We introduce the problem of Task Assignment and
                 Sequencing, which models online optimization in expert
                 crowdsourcing settings that involve non-decomposable
                 macrotasks. Non-decomposition is a property of certain
                 types of complex problems, like the formulation of an
                 R\\&D approach or the definition of a research
                 methodology, which cannot be handled through the
                 ``divide-and-conquer'' approach typically used in
                 microtask crowdsourcing. In contrast to splitting the
                 macrotask to multiple microtasks and allocating them to
                 several workers in parallel, our model supports the
                 sequential improvement of the macrotask one worker at a
                 time, across distinct time slots of a given timeline,
                 until a sufficient quality level is achieved. Our model
                 assumes an online environment where expert workers are
                 available only at specific time slots and worker/task
                 arrivals are not known a priori. With respect to this
                 setting, we propose TAS-ONLINE, an online algorithm
                 that aims to complete as many tasks as possible within
                 budget, required quality, and a given timeline, without
                 any future input information regarding job release
                 dates or worker availabilities. Experimental results
                 comparing TAS-ONLINE to five benchmarks show that it
                 achieves more completed jobs, lower flow times, and
                 higher job quality. This work bears practical
                 implications for providing performance and quality
                 guarantees to expert crowdsourcing platforms that wish
                 to integrate non-decomposable macrotasks into their
                 offered services.",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Tolmie:2018:MAP,
  author =       "Peter Tolmie and Rob Procter and Mark Rouncefield and
                 Maria Liakata and Arkaitz Zubiaga",
  title =        "Microblog Analysis as a Program of Work",
  journal =      j-TSC,
  volume =       "1",
  number =       "1",
  pages =        "2:1--2:??",
  month =        feb,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3162956",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:50 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3162956",
  acknowledgement = ack-nhfb,
  articleno =    "2",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Garimella:2018:QCS,
  author =       "Kiran Garimella and Gianmarco {De Francisci Morales}
                 and Aristides Gionis and Michael Mathioudakis",
  title =        "Quantifying Controversy on Social Media",
  journal =      j-TSC,
  volume =       "1",
  number =       "1",
  pages =        "3:1--3:??",
  month =        feb,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3140565",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:50 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3140565",
  abstract =     "Which topics spark the most heated debates on social
                 media? Identifying those topics is not only interesting
                 from a societal point of view but also allows the
                 filtering and aggregation of social media content for
                 disseminating news stories. In this article, we perform
                 a systematic methodological study of controversy
                 detection by using the content and the network
                 structure of social media. Unlike previous work, rather
                 than studying controversy in a single hand-picked topic
                 and using domain-specific knowledge, we take a general
                 approach to study topics in any domain. Our approach to
                 quantifying controversy is based on a graph-based
                 three-stage pipeline, which involves (i) building a
                 conversation graph about a topic, (ii) partitioning the
                 conversation graph to identify potential sides of the
                 controversy, and (iii) measuring the amount of
                 controversy from characteristics of the graph. We
                 perform an extensive comparison of controversy
                 measures, different graph-building approaches, and data
                 sources. We use both controversial and
                 non-controversial topics on Twitter, as well as other
                 external datasets. We find that our new
                 random-walk-based measure outperforms existing ones in
                 capturing the intuitive notion of controversy and show
                 that content features are vastly less helpful in this
                 task.",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Liu:2018:CSM,
  author =       "Weichen Liu and Sijia Xiao and Jacob T. Browne and
                 Ming Yang and Steven P. Dow",
  title =        "{ConsensUs}: Supporting Multi-Criteria Group Decisions
                 by Visualizing Points of Disagreement",
  journal =      j-TSC,
  volume =       "1",
  number =       "1",
  pages =        "4:1--4:??",
  month =        feb,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3159649",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:50 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3159649",
  abstract =     "Groups often face difficulty reaching consensus. For
                 complex decisions with multiple criteria, verbal and
                 written discourse alone may impede groups from
                 pinpointing and moving past fundamental disagreements.
                 To help support consensus building, we introduce
                 ConsensUs, a novel visualization tool that highlights
                 disagreement by asking group members to quantify their
                 subjective opinions across multiple criteria. To
                 evaluate this approach, we conducted a between-subjects
                 experiment with 87 participants on a comparative hiring
                 task. The study compared three modes of sensemaking on
                 a group decision: written discourse only, visualization
                 only, and written discourse plus visualization. We
                 confirmed that the visualization helped participants
                 identify disagreements within the group and then
                 measured subsequent changes to their individual
                 opinions. The results show that disagreement
                 highlighting led participants to align their ratings
                 more with the opinions of other group members. While
                 disagreement highlighting led to better score
                 alignment, participants reported a number of reasons
                 for shifting their score, from genuine consensus to
                 appeasement. We discuss further research angles to
                 understand how disagreement highlighting affects social
                 processes and whether it produces objectively better
                 decisions.",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Morstatter:2018:IFB,
  author =       "Fred Morstatter and Liang Wu and Uraz Yavanoglu and
                 Stephen R. Corman and Huan Liu",
  title =        "Identifying Framing Bias in Online News",
  journal =      j-TSC,
  volume =       "1",
  number =       "2",
  pages =        "5:1--5:??",
  month =        jun,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3204948",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3204948",
  abstract =     "It has been observed that different media outlets
                 exert bias in the way they report the news, which
                 seamlessly influences the way that readers' knowledge
                 is built through filtering what we read. Therefore,
                 understanding bias in news media is fundamental for
                 obtaining a holistic view of a news story. Traditional
                 work has focused on biases in terms of ``agenda
                 setting,'' where more attention is allocated to stories
                 that fit their biased narrative. The corresponding
                 method is straightforward, since the bias can be
                 detected through counting the occurrences of different
                 stories/themes within the documents. However, these
                 methods are not applicable to biases which are implicit
                 in wording, namely, ``framing'' bias. According to
                 framing theory, biased communicators will select and
                 emphasize certain facts and interpretations over others
                 when telling their story. By focusing on facts and
                 interpretations that conform to their bias, they can
                 tell the story in a way that suits their narrative.
                 Automatic detection of framing bias is challenging
                 since nuances in the wording can change the
                 interpretation of the story. In this work, we aim to
                 investigate how the subtle pattern hidden in language
                 use of a news agency can be discovered and further
                 leveraged to detect frames. In particular, we aim to
                 identify the type and polarity of frame in a sentence.
                 Extensive experiments are conducted on real-world data
                 from different countries. A case study is further
                 provided to reveal possible applications of the
                 proposed method.",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Simperl:2018:VCS,
  author =       "Elena Simperl and Neal Reeves and Chris Phethean and
                 Todd Lynes and Ramine Tinati",
  title =        "Is Virtual Citizen Science A Game?",
  journal =      j-TSC,
  volume =       "1",
  number =       "2",
  pages =        "6:1--6:??",
  month =        jun,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3209960",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3209960",
  abstract =     "The use of game elements within virtual citizen
                 science is increasingly common, promising to bring
                 increased user activity, motivation, and engagement to
                 large-scale scientific projects. However, there is an
                 ongoing debate about whether or not gamifying systems
                 such as these is actually an effective means by which
                 to increase motivation and engagement in the long term.
                 While gamification itself is receiving a large amount
                 of attention, there has been little beyond individual
                 studies to assess its suitability or success for
                 citizen science; similarly, while frameworks exist for
                 assessing citizen science performance, they tend to
                 lack any appreciation of the effects that game elements
                 might have had. We therefore review the literature to
                 determine what the trends are regarding the performance
                 of particular game elements or characteristics in
                 citizen science, and survey existing projects to assess
                 how popular different game features are. Investigating
                 this phenomenon further, we then present the results of
                 a series of interviews carried out with the EyeWire
                 citizen science project team to understand more about
                 how gamification elements are introduced, monitored,
                 and assessed in a live project. Our findings suggest
                 that projects use a range of game elements with points
                 and leaderboards the most popular, particularly in
                 projects that describe themselves as ``games.''
                 Currently, gamification appears to be effective in
                 citizen science for maintaining engagement with
                 existing communities, but shows limited impact for
                 attracting new players.",
  acknowledgement = ack-nhfb,
  articleno =    "6",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Shi:2018:MPR,
  author =       "Chuan Shi and Jian Liu and Yiding Zhang and Binbin Hu
                 and Shenghua Liu and Philip S. Yu",
  title =        "{MFPR}: A Personalized Ranking Recommendation with
                 Multiple Feedback",
  journal =      j-TSC,
  volume =       "1",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jun,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3216368",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3216368",
  abstract =     "Recently, recommender systems have played an important
                 role in improving web user experiences and increasing
                 profits. Recommender systems exploit users' behavioral
                 history (i.e., feedback on items) to build models. The
                 feedback usually includes explicit feedback (e.g.,
                 ratings) and implicit feedback (e.g., browsing history,
                 click logs), which are both useful for improving
                 recommendations. However, as far as we are concerned,
                 no existing works have integrated both explicit and
                 multiple implicit feedback simultaneously. Therefore,
                 we propose a unified and flexible model, named Multiple
                 Feedback-based Personalized Ranking (MFPR), to make
                 full use of multiple feedback, which uses a
                 personalized ranking framework. To train model MFPR, we
                 design an algorithm to generate ordered item pairs as
                 labeled data, with consideration of both rating scores
                 and multiple implicit feedback. Extensive experiments
                 on two real-world datasets validate the effectiveness
                 of the MFPR model. With the integration of multiple
                 feedback, MFPR significantly improves recommendation
                 performance.",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Tausczik:2018:ECS,
  author =       "Yla Tausczik and Rosta Farzan and John Levine and
                 Robert Kraut",
  title =        "Effects of Collective Socialization on Newcomers'
                 Response to Feedback in Online Communities",
  journal =      j-TSC,
  volume =       "1",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jun,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3191834",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3191834",
  abstract =     "Collective socialization involves introducing new
                 members to an organization as a group or cohort. In
                 traditional offline organizations, collective
                 socialization is a standard and effective socialization
                 strategy. This article investigates the impact of
                 collective socialization on newcomers' motivation and
                 learning in an online community and the effect it has
                 on newcomers' reaction to feedback from the community.
                 One observational field study and two random-assignment
                 experiments involving editing Wikipedia articles show
                 that collective socialization altered the way newcomers
                 responded to feedback from the community. The
                 observational study of students editing Wikipedia
                 articles as part of a classroom assignment found that
                 those who worked relatively independently without peer
                 support made more edits in response to critical,
                 negative feedback, presumably to fix errors, whereas
                 students who had peer support did not. Two experiments
                 in which Mechanical Turk workers edited Wikipedia
                 articles independently or in a group found that working
                 in a group diffused the impact of both positive and
                 negative feedback. We discuss these findings, which
                 highlight the importance of considering the negative
                 consequences of introducing a new socialization
                 practice to an online community.",
  acknowledgement = ack-nhfb,
  articleno =    "8",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Robert:2018:GSI,
  author =       "Lionel P. {Robert, Jr.} and Andrea Forte and Claudia
                 M{\"u}ller and Michael Prilla and Adriana S. Vivacqua",
  title =        "{GROUP 2018} Special Issue Guest Editorial: Another 25
                 Years of {GROUP}",
  journal =      j-TSC,
  volume =       "1",
  number =       "3",
  pages =        "9:1--9:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3290870",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3290870",
  abstract =     "For over 25 years, the ACM International Conference on
                 Supporting Group Work (GROUP) has been and will
                 continue to be the premier venue for research on
                 Computer-Supported Cooperative Work, Human--Computer
                 Interaction, Computer-Supported Collaborative Learning,
                 and Socio-Technical Studies. The three papers in this
                 special issue demonstrate GROUP's continued commitment
                 to diverse research approaches, emerging technologies,
                 and collaborative work. We hope you enjoy these papers
                 and, like us, look forward to another 25 years of
                 GROUP.",
  acknowledgement = ack-nhfb,
  articleno =    "9",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Jabbar:2018:PIV,
  author =       "Karim Jabbar and Pernille Bj{\o}rn",
  title =        "Permeability, Interoperability, and Velocity:
                 Entangled Dimensions of Infrastructural Grind at the
                 Intersection of Blockchain and Shipping",
  journal =      j-TSC,
  volume =       "1",
  number =       "3",
  pages =        "10:1--10:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3288800",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
                 http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3288800",
  abstract =     "Blockchain can potentially be appropriated as a social
                 computing technology, which enables transactions across
                 people and artefacts via a large socio-technical
                 information infrastructure constituted by the actions
                 of multiple people and computers. However, Blockchain
                 is not a social computing technology a priori; instead
                 to emerge as one, much effort and work is required to
                 radically transform existing domains, including
                 wrestling with traditions, standards, and legacy. In
                 this article, we expand on previous work on Blockchain
                 as an information infrastructure, and on the notion of
                 infrastructural grind. Infrastructural grind allows us
                 to analytically explore how the emerging Blockchain
                 technology is appropriated into established business
                 domains, in our case the shipping industry. We present
                 ethnographic data unpacking three different accounts of
                 infrastructural grind taking place at the intersection
                 of the shipping and the Blockchain information
                 infrastructures. The results demonstrate that
                 infrastructural grind occurs as a result of various
                 infrastructuring activities taking place at different
                 intersections between the two infrastructures and is
                 constituted of the sum of these activities. We propose
                 a framework in which infrastructural grind is
                 constituted of three entangled dimensions:
                 permeability, interoperability, and velocity. These
                 socio-technical dimensions relate to infrastructural
                 properties such as legacy, embeddedness, and standards,
                 as well as to technical properties of specific
                 solutions deployed at specific points of
                 infrastructural grind. Our analysis shows that these
                 dimensions are enacted differently along the shipping
                 supply chain, and depending on the dynamic interplay
                 between them at various points of infrastructural
                 grind. At different points in time, the infrastructural
                 grind between Blockchain and the shipping domain will
                 thus manifest itself differently and at differential
                 velocity.",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Yu:2018:MUI,
  author =       "Xianqi Yu and Yuqing Sun and Elisa Bertino and Xin
                 Li",
  title =        "Modeling User Intrinsic Characteristic on Social Media
                 for Identity Linkage",
  journal =      j-TSC,
  volume =       "1",
  number =       "3",
  pages =        "11:1--11:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3267442",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3267442",
  abstract =     "Most users on social media have intrinsic
                 characteristics, such as interests and political views,
                 that can be exploited to identify and track them, thus
                 raising privacy and identity concerns in online
                 communities. In this article, we investigate the
                 problem of user identity linkage on two behavior
                 datasets collected from different experiments.
                 Specifically, we focus on user linkage based on users'
                 interaction behaviors with respect to content topics.
                 We propose an embedding method to model a topic as a
                 vector in a latent space to interpret its deep
                 semantics. Then a user is modeled as a vector based on
                 his or her interactions with topics. The embedding
                 representations of topics are learned by optimizing the
                 joint-objective: the compatibility between topics with
                 similar semantics, the discriminative abilities of
                 topics to distinguish identities, and the consistency
                 of the same user's characteristics from two datasets.
                 The effectiveness of our method is verified on
                 real-life datasets and the results show that it
                 outperforms related methods. We also analyze failure
                 cases in the application of our identity linkage
                 method. Our analysis shows that factors such as the
                 visibility and variance of user behaviors and users'
                 group psychology can result in mis-linkages. We also
                 analyze the details of the behaviors of some
                 representative users to understand the essential
                 reasons for their identity being mis-linked. We find
                 that these users have high variance level in their
                 behaviors. According to the above experimental results,
                 we introduce a confidence score into identity linkage
                 to provide information about the accuracy of the method
                 results.",
  acknowledgement = ack-nhfb,
  articleno =    "11",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Jan:2018:APD,
  author =       "Steve T. K. Jan and Chun Wang and Qing Zhang and Gang
                 Wang",
  title =        "Analyzing Payment-Driven Targeted {Q\&A} Systems",
  journal =      j-TSC,
  volume =       "1",
  number =       "3",
  pages =        "12:1--12:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3281449",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3281449",
  abstract =     "Today's online question and answer (Q\&A) services are
                 receiving a large volume of questions. It becomes
                 increasingly challenging to motivate domain experts to
                 provide quick and high-quality answers. Recent systems
                 seek to engage real-world experts by allowing them to
                 set a price on their answers. This leads to a
                 ``targeted'' Q\&A model where users ask questions to a
                 target expert by paying the corresponding price. In
                 this article, we perform a case study on two emerging
                 targeted Q\&A systems, Fenda (China) and Whale (U.S.),
                 to understand how monetary incentives affect user
                 behavior. By analyzing a large dataset of 220K
                 questions (worth 1 million USD), we find that payments
                 indeed enable quick answers from experts, but also
                 drive certain users to game the system for profits. In
                 addition, this model requires users (experts) to
                 proactively adjust their price to make profits. People
                 who are unwilling to lower their prices are likely to
                 hurt their income and engagement over time.",
  acknowledgement = ack-nhfb,
  articleno =    "12",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Santani:2018:LSL,
  author =       "Darshan Santani and Salvador Ruiz-Correa and Daniel
                 Gatica-Perez",
  title =        "Looking South: Learning Urban Perception in Developing
                 Cities",
  journal =      j-TSC,
  volume =       "1",
  number =       "3",
  pages =        "13:1--13:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3224182",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:51 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3224182",
  abstract =     "Mobile and social technologies are providing new
                 opportunities to document, characterize, and gather
                 impressions of urban environments. In this article, we
                 present a study that examines urban perceptions of
                 three cities in central Mexico; the study integrates a
                 mobile crowdsourcing framework to collect geo-localized
                 images of urban environments by a local youth
                 community, an online crowdsourcing platform to gather
                 impressions of urban environments along 12 physical and
                 psychological dimensions, and a deep learning framework
                 to automatically infer human impressions of outdoor
                 urban scenes. Our study resulted in a collection of
                 7,000 geo-localized images containing outdoor scenes
                 and views of each city's built environment, including
                 touristic, historical, and residential neighborhoods,
                 and 144,000 individual judgments from Amazon Mechanical
                 Turk. Statistical analyses show that outdoor
                 environments can be assessed in terms of interrater
                 agreement for most of the urban dimensions by the
                 observers of crowdsourced images. Furthermore, we
                 proposed a methodology to automatically infer human
                 perceptions of outdoor scenes using a variety of
                 low-level image features and generic deep learning
                 (CNN) features. We found that CNN features consistently
                 outperformed all the individual low-level image
                 features for all the studied urban dimensions. We
                 obtained a maximum R 2 of 0.49 using CNN features; for
                 9 out of 12 labels, the obtained R 2 values exceeded
                 0.44.",
  acknowledgement = ack-nhfb,
  articleno =    "13",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Crowston:2018:LPU,
  author =       "Kevin Crowston and Xuefei (Nancy) Deng and Yoram M.
                 Kalman",
  title =        "A Librarian, a Politician, a {UX} Expert, and a
                 Cyberbully Walk into a Special Issue",
  journal =      j-TSC,
  volume =       "1",
  number =       "4",
  pages =        "14:1--14:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3293613",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3293613",
  acknowledgement = ack-nhfb,
  articleno =    "14e",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Gasson:2018:PAS,
  author =       "Susan Gasson and Michelle Purcelle",
  title =        "A Participation Architecture to Support User
                 Peripheral Participation in a Hybrid {FOSS} Community",
  journal =      j-TSC,
  volume =       "1",
  number =       "4",
  pages =        "14:1--14:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3290837",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3290837",
  abstract =     "Participation by product users is critical to success
                 in free, open-source software (FOSS) software
                 communities as they originate and develop valuable
                 ideas for product innovation that are unlikely to
                 originate from the core software development community.
                 Users tend to be involved at the periphery of FOSS
                 communities, suggesting new product ideas, highlighting
                 problems with user documentation, or explaining when
                 the product design fails to fit with the needs of their
                 local user application domain. As an increasing number
                 of FOSS projects employ a hybrid participation model
                 that combines volunteer effort with paid software
                 development effort or product support, it can be
                 difficult for non-developer users to participate in
                 product innovation. In colocated organizations, it is
                 theorized that peripheral participants learn how to
                 engage with the practices and cultural identity of a
                 community through a sociocultural apprenticeship known
                 as legitimate peripheral participation. But we have
                 little literature that explores how legitimate
                 peripheral participation is enabled in online
                 communities. The research study presented in this
                 article explores how participation by peripheral users
                 in a hybrid FOSS project is afforded by participation
                 architecture channels and community mechanisms that
                 mediate two forms of engagement: a ``cognitive
                 apprenticeship'' that introduces participants to
                 situated domain activity, such as the community
                 processes involved in product innovation, and a
                 ``social apprenticeship'' by which participants become
                 enculturated in the system of meanings, values, norms,
                 and behaviors that govern community/participant
                 identity. We identified five stages of community
                 innovation, analyzing sociotechnical affordances of the
                 online participation architecture that enable
                 peripheral participants to internalize the meanings of
                 community practice and to develop a social identity
                 within the FOSS community. Our contribution to theory
                 is provided by the substantive explanation of the
                 cognitive and social translations that enable
                 legitimate peripheral participation in online
                 communities, mediated by sociotechnical access channels
                 and mechanisms that afford two contrasting forms of
                 opportunities for action: those resulting from
                 interactions between a goal-oriented actor and the
                 technology platform features or channels of
                 participation, and those associated with the social
                 structures, roles, and relationships underpinning
                 community interactions. Neither of these is sufficient
                 without the other. Our contribution to practice is
                 provided by an explanation of how four distinct
                 categories of affordance provide these cognitive and
                 social apprenticeship benefits, allowing participation
                 architecture designers to cater to all forms of
                 peripheral user participation. We conclude that the
                 technical affordances of a typical FOSS community
                 participation architecture are insufficient to mediate
                 peripheral participation by nontechnical users.
                 Meaningful participation is mediated by interactions
                 between boundary spanners who play knowledge-brokering
                 and organizational bridging roles. The combination of
                 technical and social affordances enables peripheral
                 participants to acquire an interior view of community
                 practices and social culture and in turn to introduce
                 new ideas, new values, and new rationales to produce a
                 generative dance of innovation that percolates through
                 the community.",
  acknowledgement = ack-nhfb,
  articleno =    "14",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Tahmasbi:2018:SCA,
  author =       "Nargess Tahmasbi and Elham Rastegari",
  title =        "A Socio-Contextual Approach in Automated Detection of
                 Public Cyberbullying on {Twitter}",
  journal =      j-TSC,
  volume =       "1",
  number =       "4",
  pages =        "15:1--15:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3290838",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3290838",
  abstract =     "Cyberbullying is a major cyber issue that is common
                 among adolescents. Recent reports show that more than
                 one out of five students in the United States is a
                 victim of cyberbullying. Majority of cyberbullying
                 incidents occur on public social media platforms such
                 as Twitter. Automated cyberbullying detection methods
                 can help prevent cyberbullying before the harm is done
                 on the victim. In this study, we analyze two corpora of
                 cyberbullying tweets from similar incidents to
                 construct and validate an automated detection model.
                 Our method emphasizes the two claims that are supported
                 by our results. First, despite other approaches that
                 assume that cyberbullying instances use vulgar or
                 profane words, we show that they do not necessarily
                 contain negative words. Second, we highlight the
                 importance of context and the characteristics of actors
                 involved and their position in the network structure in
                 detecting cyberbullying rather than only considering
                 the textual content in our analysis.",
  acknowledgement = ack-nhfb,
  articleno =    "15",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Bay:2018:SME,
  author =       "Morten Bay",
  title =        "Social Media Ethics: a {Rawlsian} Approach to
                 Hypertargeting and Psychometrics in Political and
                 Commercial Campaigns",
  journal =      j-TSC,
  volume =       "1",
  number =       "4",
  pages =        "16:1--16:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3281450",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3281450",
  abstract =     "Targeted social media advertising based on
                 psychometric user profiling has emerged as an effective
                 way of reaching individuals who are predisposed to
                 accept and be persuaded by the advertising message.
                 This article argues that in the case of political
                 advertising, this may present a democratic and ethical
                 challenge. Hypertargeting methods such as psychometrics
                 can ``crowd out'' political communication with opposing
                 views due to individual attention and time limitations,
                 creating inequities in the access to information
                 essential for voting decisions. The use of
                 psychometrics also appears to have been used to spread
                 both information and misinformation through social
                 media in recent elections in the U.S. and Europe. This
                 article is an applied ethics study of these methods in
                 the context of democratic processes and compared to
                 purely commercial situations. The ethical approach is
                 based on the theoretical, contractarian work of John
                 Rawls, which serves as a lens through which the author
                 examines whether the rights of individuals, as Rawls
                 attributes them, are violated by this practice. The
                 article concludes that within a Rawlsian framework, use
                 of psychometrics in commercial advertising on social
                 media platforms, though not immune to criticism, is not
                 necessarily unethical. In a democracy, however, the
                 individual cannot abandon the consumption of political
                 information, and since using psychometrics in political
                 campaigning makes access to such information unequal,
                 it violates Rawlsian ethics and should be regulated.",
  acknowledgement = ack-nhfb,
  articleno =    "16",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Kou:2018:USR,
  author =       "Yubo Kou and Colin M. Gray and Austin L. Toombs and
                 Robin S. Adams",
  title =        "Understanding Social Roles in an Online Community of
                 Volatile Practice: A Study of User Experience
                 Practitioners on Reddit",
  journal =      j-TSC,
  volume =       "1",
  number =       "4",
  pages =        "17:1--17:??",
  month =        dec,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3283827",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3283827",
  abstract =     "Community of practice (CoP) is a primary framework in
                 social computing research that addresses learning and
                 organizing specific practices in online communities.
                 However, the classic CoP theory does not provide a
                 detailed account for how practices change or evolve.
                 Against the backdrop of a rapidly changing occupational
                 landscape, it is crucial to understand how people
                 participate in online communities focused on practices
                 that have a volatile nature, as well as how social
                 computing tools can best support them. In this article,
                 we examine user experience (UX) design as a volatile
                 practice that has no coherent body of knowledge and
                 lacks a concrete path for newcomers to become a UX
                 professional. Our study site is the
                 ``/r/userexperience'' subreddit, an online UX community
                 where practitioners socialize and learn. Using a
                 mixed-methods approach, we identified five distinct
                 social roles in relation to knowledge production and
                 dissemination in the online community of volatile
                 practice. We demonstrate that knowledge production is
                 highly distributed, involving the participation and
                 sensemaking of community members of varied levels of
                 experience. We discuss how online platforms support
                 online community of volatile practice and how our
                 findings contribute to the CoP literature.",
  acknowledgement = ack-nhfb,
  articleno =    "17",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Saldivar:2019:OIM,
  author =       "Jorge Saldivar and Florian Daniel and Luca Cernuzzi
                 and Fabio Casati",
  title =        "Online Idea Management for Civic Engagement: A Study
                 on the Benefits of Integration with Social Networking",
  journal =      j-TSC,
  volume =       "2",
  number =       "1",
  pages =        "1:1--1:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3284982",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3284982",
  abstract =     "Idea Management (IM) has increasingly been adopted in
                 the civic domain as a tool to engage the citizenry in
                 processes oriented toward innovating plans, policies,
                 and services. While Idea Management Systems (IMSs), the
                 software systems that instrument IM, definitely help
                 manage this practice, they require citizens to be
                 committed to a separate virtual space for which they
                 need to register, they must learn how to operate it,
                 and they must return to it frequently. This article
                 presents an approach that integrates IMS with today's
                 most popular digital spaces of participation, the
                 social networking sites, thus enabling citizens to
                 engage in IM processes using ordinary tools and without
                 having to step outside their daily habits. Our goal is
                 to reach out and pull into IM those large and
                 demographically diverse sectors of the society that are
                 already present and participating in social networking
                 sites. Through a real case study of IM in the public
                 sector that mixed both qualitative and quantitative
                 data collection methods, our proposal demonstrates a
                 promising approach to reduce the barriers of
                 participation. We conclude with an analysis of the
                 strengths and limitations of our proposal.",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Ruiz-Correa:2019:MCC,
  author =       "Salvador Ruiz-Correa and Itzia Ruiz-Correa and Carlo
                 Olmos-Carrillo and Fatima Alba Rend{\'o}n-Huerta and
                 Beatriz Ramirez-Salazar and Laurent Son Nguyen and
                 Daniel Gatica-Perez",
  title =        "Mi Casa es su Casa? {Examining} {Airbnb} Hospitality
                 Exchange Practices in a Developing Economy",
  journal =      j-TSC,
  volume =       "2",
  number =       "1",
  pages =        "2:1--2:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3299817",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3299817",
  abstract =     "We present a study involving twenty in-depth,
                 semi-structured interviews, a street survey, and online
                 data to understand Airbnb hospitality exchange
                 practices in the context of a developing country. As
                 case studies, we investigate Airbnb practices of both
                 hosts and guests in two tourist venues in Mexico -- the
                 eighth most visited country worldwide. The analysis of
                 the data revealed that Airbnb practices in Mexico have
                 some similarities but also important differences with
                 those previously reported in the literature. We found
                 (1) that money is the main motivation for hosts to
                 participate in Airbnb and that the earned money
                 contributes significantly to the overall income of
                 hosts; (2) that traditions that permeate the Mexican
                 culture motivate hosts to engage in more personal
                 hospitality experiences; (3) that Airbnb host practices
                 lead to the creation of informal jobs that support the
                 local community; and (4) that Airbnb local guests
                 suggest that the lack of contextual information (i.e.,
                 the characteristics of the neighborhood where the
                 accommodation is located) is a problem when renting in
                 Mexico owing to safety issues.",
  acknowledgement = ack-nhfb,
  articleno =    "2",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Difallah:2019:DAF,
  author =       "Djellel Difallah and Alessandro Checco and Gianluca
                 Demartini and Philippe Cudr{\'e}-Mauroux",
  title =        "Deadline-Aware Fair Scheduling for Multi-Tenant
                 Crowd-Powered Systems",
  journal =      j-TSC,
  volume =       "2",
  number =       "1",
  pages =        "3:1--3:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3301003",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3301003",
  abstract =     "Crowdsourcing has become an integral part of many
                 systems and services that deliver high-quality results
                 for complex tasks such as data linkage, schema
                 matching, and content annotation. A standard function
                 of such crowd-powered systems is to publish a batch of
                 tasks on a crowdsourcing platform automatically and to
                 collect the results once the workers complete them.
                 Currently, these systems provide limited guarantees
                 over the execution time, which is problematic for many
                 applications. Timely completion may even be impossible
                 to guarantee due to factors specific to the
                 crowdsourcing platform, such as the availability of
                 workers and concurrent tasks. In our previous work, we
                 presented the architecture of a crowd-powered system
                 that reshapes the interaction mechanism with the crowd.
                 Specifically, we studied a push-crowdsourcing model
                 whereby the workers receive tasks instead of selecting
                 them from a portal. Based on this interaction model, we
                 employed scheduling techniques similar to those found
                 in distributed computing infrastructures to automate
                 the task assignment process. In this work, we first
                 devise a generic scheduling strategy that supports both
                 fairness and deadline-awareness. Second, to complement
                 the proof-of-concept experiments previously performed
                 with the crowd, we present an extensive set of
                 simulations meant to analyze the properties of the
                 proposed scheduling algorithms in an environment with
                 thousands of workers and tasks. Our experimental
                 results show that, by accounting for human factors,
                 micro-task scheduling can achieve fairness for
                 best-effort batches and boosts production batches.",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Santos:2019:AAQ,
  author =       "Tiago Santos and Simon Walk and Roman Kern and Markus
                 Strohmaier and Denis Helic",
  title =        "Activity Archetypes in Question-and-Answer ({Q\&A})
                 {Websites} --- A Study of 50 {Stack Exchange}
                 Instances",
  journal =      j-TSC,
  volume =       "2",
  number =       "1",
  pages =        "4:1--4:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3301612",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3301612",
  abstract =     "Millions of users on the Internet discuss a variety of
                 topics on Question-and-Answer (Q\&A) instances.
                 However, not all instances and topics receive the same
                 amount of attention, as some thrive and achieve
                 self-sustaining levels of activity, while others fail
                 to attract users and either never grow beyond being a
                 small niche community or become inactive. Hence, it is
                 imperative to not only better understand but also to
                 distill deciding factors and rules that define and
                 govern sustainable Q\&A instances. We aim to empower
                 community managers with quantitative methods for them
                 to better understand, control, and foster their
                 communities, and thus contribute to making the Web a
                 more efficient place to exchange information. To that
                 end, we extract, model, and cluster a user
                 activity-based time series from 50 randomly selected
                 Q\&A instances from the Stack Exchange network to
                 characterize user behavior. We find four distinct types
                 of user activity temporal patterns, which vary
                 primarily according to the users' activity frequency.
                 Finally, by breaking down total activity in our 50 Q\&A
                 instances by the previously identified user activity
                 profiles, we classify those 50 Q\&A instances into
                 three different activity profiles. Our parsimonious
                 categorization of Q\&A instances aligns with the stage
                 of development and maturity of the underlying
                 communities, and can potentially help operators of such
                 instances: We not only quantitatively assess progress
                 of Q\&A instances, but we also derive practical
                 implications for optimizing Q\&A community building
                 efforts, as we, e.g., recommend which user types to
                 focus on at different developmental stages of a Q\&A
                 community.",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Cho:2019:UBF,
  author =       "Jin-Hee Cho and Scott Rager and John O'Donovan and
                 Sibel Adali and Benjamin D. Horne",
  title =        "Uncertainty-based False Information Propagation in
                 Social Networks",
  journal =      j-TSC,
  volume =       "2",
  number =       "2",
  pages =        "5:1--5:??",
  month =        oct,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3311091",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3311091",
  abstract =     "Many network scientists have investigated the problem
                 of mitigating or removing false information propagated
                 in social networks. False information falls into two
                 broad categories: disinformation and misinformation.
                 Disinformation represents false information that is
                 knowingly shared and distributed with malicious intent.
                 Misinformation in contrast is false information shared
                 unwittingly, without any malicious intent. Many
                 existing methods to mitigate or remove false
                 information in networks concentrate on methods to find
                 a set of seeding nodes (or agents) based on their
                 network characteristics (e.g., centrality features) to
                 treat. The aim of these methods is to disseminate
                 correct information in the most efficient way. However,
                 little work has focused on the role of uncertainty as a
                 factor in the formulation of agents' opinions.
                 Uncertainty-aware agents can form different opinions
                 and eventual beliefs about true or false information
                 resulting in different patterns of information
                 diffusion in networks. In this work, we leverage an
                 opinion model, called Subjective Logic (SL), which
                 explicitly deals with a level of uncertainty in an
                 opinion where the opinion is defined as a combination
                 of belief, disbelief, and uncertainty, and the level of
                 uncertainty is easily interpreted as a person's
                 confidence in the given belief or disbelief. However,
                 SL considers the dimension of uncertainty only derived
                 from a lack of information (i.e., ignorance), not from
                 other causes, such as conflicting evidence. In the era
                 of Big Data, where we are flooded with information,
                 conflicting information can increase uncertainty (or
                 ambiguity) and have a greater effect on opinions than a
                 lack of information (or ignorance). To enhance the
                 capability of SL to deal with ambiguity as well as
                 ignorance, we propose an SL-based opinion model that
                 includes a level of uncertainty derived from both
                 causes. By developing a variant of the
                 Susceptible-Infected-Recovered epidemic model that can
                 change an agent's status based on the state of their
                 opinions, we capture the evolution of agents' opinions
                 over time. We present an analysis and discussion of
                 critical changes in network outcomes under varying
                 values of key design parameters, including the
                 frequency ratio of true or false information
                 propagation, centrality metrics used for selecting
                 seeding false informers and true informers, an opinion
                 decay factor, the degree of agents' prior belief, and
                 the percentage of true informers. We validated our
                 proposed opinion model using both the synthetic network
                 environments and realistic network environments
                 considering a real network topology, user behaviors,
                 and the quality of news articles. The proposed agent's
                 opinion model and corresponding strategies to deal with
                 false information can be applicable to combat the
                 spread of fake news in various social media platforms
                 (e.g., Facebook).",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Feyisetan:2019:BMI,
  author =       "Oluwaseyi Feyisetan and Elena Simperl",
  title =        "Beyond Monetary Incentives: Experiments in Paid
                 Microtask Contests",
  journal =      j-TSC,
  volume =       "2",
  number =       "2",
  pages =        "6:1--6:??",
  month =        oct,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3321700",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3321700",
  abstract =     "In this article, we aim to gain a better understanding
                 into how paid microtask crowdsourcing could leverage
                 its appeal and scaling power by using contests to boost
                 crowd performance and engagement. We introduce our
                 microtask-based annotation platform Wordsmith, which
                 features incentives such as points, leaderboards, and
                 badges on top of financial remuneration. Our analysis
                 focuses on a particular type of incentive, contests, as
                 a means to apply crowdsourcing in near-real-time
                 scenarios, in which requesters need labels quickly. We
                 model crowdsourcing contests as a continuous-time
                 Markov chain with the objective to maximise the output
                 of the crowd workers, while varying a parameter that
                 determines whether a worker is eligible for a reward
                 based on their present rank on the leaderboard. We
                 conduct empirical experiments in which crowd workers
                 recruited from CrowdFlower carry out annotation
                 microtasks on Wordsmith-in our case, to identify named
                 entities in a stream of Twitter posts. In the
                 experimental conditions, we test different reward
                 spreads and record the total number of annotations
                 received. We compare the results against a control
                 condition in which the same annotation task was
                 completed on CrowdFlower without a time or contest
                 constraint. The experiments show that rewarding only
                 the best contributors in a live contest could be a
                 viable model to deliver results faster, though quality
                 might suffer for particular types of annotation tasks.
                 Increasing the reward spread leads to more work being
                 completed, especially by the top contestants. Overall,
                 the experiments shed light on possible design
                 improvements of paid microtasks platforms to boost task
                 performance and speed and make the overall experience
                 more fair and interesting for crowd workers.",
  acknowledgement = ack-nhfb,
  articleno =    "6",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Wu:2019:DNP,
  author =       "Qunfang Wu and Yisi Sang and Yun Huang",
  title =        "Danmaku: A New Paradigm of Social Interaction via
                 Online Videos",
  journal =      j-TSC,
  volume =       "2",
  number =       "2",
  pages =        "7:1--7:??",
  month =        oct,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3329485",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3329485",
  abstract =     "Danmaku is a new commentary design for online videos.
                 Unlike traditional forums where comments are displayed
                 asynchronously below a video screen in order of when
                 the comments are posted, danmaku comments are overlaid
                 on the screen and displayed along with the video. This
                 new design creates a pseudo-synchronous effect by
                 displaying asynchronous comments with certain video
                 segments in a synchronous fashion, and the links
                 between danmaku comments and the video segments are
                 defined by users. Danmaku is gaining popularity;
                 however, little is known, compared to the traditional
                 forum design, regarding how effective the new danmaku
                 design is in promoting social interactions among online
                 users. In this work, we collected 38,399 danmaku
                 comments and 16,414 forum comments posted in 2017 on 30
                 popular videos on Bilibili.com. We compared user
                 participation from different perspectives, e.g., number
                 of comments, sentiment of the comments, language
                 patterns, and ways of knowledge sharing. Our results
                 showed that compared to the traditional linear design,
                 the danmaku design significantly promoted user
                 participation, i.e., there were more users and more
                 comments in danmaku. Additionally, active users posted
                 more positive comments, though they were anonymous;
                 more linguistic memes were used in danmaku, suggesting
                 that it was used to facilitate community-building. In
                 addition to its effectiveness in promoting social
                 interactions, our results also show that danmaku and
                 forum designs play complementary roles in knowledge
                 sharing, where danmaku comments involved more explicit
                 (know-what) knowledge sharing, and forum comments
                 exhibited more tacit (know-how) knowledge sharing. Our
                 findings contribute to the development of social
                 presence theory and have design implications for better
                 social interaction via online videos.",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Posch:2019:MMC,
  author =       "Lisa Posch and Arnim Bleier and Clemens M. Lechner and
                 Daniel Danner and Fabian Fl{\"o}ck and Markus
                 Strohmaier",
  title =        "Measuring Motivations of Crowdworkers: The
                 Multidimensional Crowdworker Motivation Scale",
  journal =      j-TSC,
  volume =       "2",
  number =       "2",
  pages =        "8:1--8:??",
  month =        oct,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3335081",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:52 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3335081",
  abstract =     "Crowd employment is a new form of short-term and
                 flexible employment that has emerged during the past
                 decade. To understand this new form of employment, it
                 is crucial to illuminate the underlying motivations of
                 the workforce involved in it. This article introduces
                 the Multidimensional Crowdworker Motivation Scale
                 (MCMS), a scale for measuring the motivation of
                 crowdworkers on microtask platforms. The MCMS is
                 theoretically grounded in self-determination theory and
                 tailored specifically to the context of paid
                 crowdsourced microlabor. The scale measures the
                 motivation of crowdworkers along six motivational
                 dimensions, ranging from amotivation to intrinsic
                 motivation. We validated the MCMS on data collected in
                 ten countries and three income groups. Factor analyses
                 demonstrated that the MCMS's six dimensions showed good
                 model fit, validity, and reliability. Furthermore, our
                 measurement invariance tests showed that motivations
                 measured with the MCMS are comparable across countries
                 and income groups, and we present a first cross-country
                 comparison of crowdworker motivations. This work
                 constitutes an important first step toward
                 understanding the motivations of the international
                 crowd workforce.",
  acknowledgement = ack-nhfb,
  articleno =    "8",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Tausczik:2019:IGS,
  author =       "Yla Tausczik and Xiaoyun Huang",
  title =        "The Impact of Group Size on the Discovery of Hidden
                 Profiles in Online Discussion Groups",
  journal =      j-TSC,
  volume =       "2",
  number =       "3",
  pages =        "10:1--10:??",
  month =        nov,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3359758",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:53 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3359758",
  abstract =     "Online discussions help individuals to gather
                 knowledge and make important decisions in diverse areas
                 from health and finance to computing and data science.
                 Online discussion groups exhibit unique group dynamics
                 not found in traditional small groups, such as
                 staggered participation and asynchronous communication,
                 and the effects of these features on knowledge sharing
                 is not well understood. In this article, we focus on
                 one such aspect: wide variation in group size. Using a
                 controlled experiment with a hidden profile task, we
                 evaluate online discussion groups' capacity to share
                 distributed knowledge when group size ranges from 4 to
                 32 participants. We found that individuals in
                 medium-sized discussions performed the best, and we
                 suggest that this represents a tradeoff in which larger
                 groups tend to share more facts, but have more
                 difficulty than smaller groups at resolving
                 misunderstandings.",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

@Article{Wright:2019:HMH,
  author =       "Darryl E. Wright and Lucy Fortson and Chris Lintott
                 and Michael Laraia and Mike Walmsley",
  title =        "Help Me to Help You: Machine Augmented Citizen
                 Science",
  journal =      j-TSC,
  volume =       "2",
  number =       "3",
  pages =        "11:1--11:??",
  month =        nov,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3362741",
  ISSN =         "2469-7818 (print), 2469-7826 (electronic)",
  bibdate =      "Fri Dec 6 16:55:53 MST 2019",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tsc.bib",
  URL =          "https://dl.acm.org/citation.cfm?id=3362741",
  abstract =     "The increasing size of datasets with which researchers
                 in a variety of domains are confronted has led to a
                 range of creative responses, including the deployment
                 of modern machine learning techniques and the advent of
                 large scale ``citizen science projects.'' However, the
                 ability of the latter to provide suitably large
                 training sets for the former is stretched as the size
                 of the problem (and competition for attention amongst
                 projects) grows. We explore the application of
                 unsupervised learning to leverage structure that exists
                 in an initially unlabelled dataset. We simulate
                 grouping similar points before presenting those groups
                 to volunteers to label. Citizen science labelling of
                 grouped data is more efficient, and the gathered labels
                 can be used to improve efficiency further for labelling
                 future data. To demonstrate these ideas, we perform
                 experiments using data from the Pan-STARRS Survey for
                 Transients (PSST) with volunteer labels gathered by the
                 Zooniverse project, Supernova Hunters and a simulated
                 project using the MNIST handwritten digit dataset. Our
                 results show that, in the best case, we might expect to
                 reduce the required volunteer effort by 87.0\% and
                 92.8\% for the two datasets, respectively. These
                 results illustrate a symbiotic relationship between
                 machine learning and citizen scientists where each
                 empowers the other with important implications for the
                 design of citizen science projects in the future.",
  acknowledgement = ack-nhfb,
  articleno =    "11",
  fjournal =     "ACM Transactions on Social Computing (TSC)",
  journal-URL =  "http://dl.acm.org/pub.cfm?id=J1546",
}

%%% TO DO: [06-Dec-2019] v2n3 is still in progress