TY - GEN A1 - Studen, Laura A1 - Tiberius, Victor T1 - Social Media, Quo Vadis? BT - Prospective Development and Implications T2 - Postprints der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - Over the past two decades, social media have become a crucial and omnipresent cultural and economic phenomenon, which has seen platforms come and go and advance technologically. In this study, we explore the further development of social media regarding interactive technologies, platform development, relationships to news media, the activities of institutional and organizational users, and effects of social media on the individual and the society over the next five to ten years by conducting an international, two-stage Delphi study. Our results show that enhanced interaction on platforms, including virtual and augmented reality, somatosensory sense, and touch- and movement-based navigation are expected. AIs will interact with other social media users. Inactive user profiles will outnumber active ones. Platform providers will diversify into the WWW, e-commerce, edu-tech, fintechs, the automobile industry, and HR. They will change to a freemium business model and put more effort into combating cybercrime. Social media will become the predominant news distributor, but fake news will still be problematic. Firms will spend greater amounts of their budgets on social media advertising, and schools, politicians, and the medical sector will increase their social media engagement. Social media use will increasingly lead to individuals’ psychic issues. Society will benefit from economic growth and new jobs, increased political interest, democratic progress, and education due to social media. However, censorship and the energy consumption of platform operators might rise. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 131 KW - Delphi study KW - individual effects KW - interactive technologies KW - news media KW - social media KW - societal effects Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-482934 SN - 1867-5808 IS - 131 ER - TY - JOUR A1 - Studen, Laura A1 - Tiberius, Victor T1 - Social Media, Quo Vadis? BT - Prospective Development and Implications JF - Future Internet N2 - Over the past two decades, social media have become a crucial and omnipresent cultural and economic phenomenon, which has seen platforms come and go and advance technologically. In this study, we explore the further development of social media regarding interactive technologies, platform development, relationships to news media, the activities of institutional and organizational users, and effects of social media on the individual and the society over the next five to ten years by conducting an international, two-stage Delphi study. Our results show that enhanced interaction on platforms, including virtual and augmented reality, somatosensory sense, and touch- and movement-based navigation are expected. AIs will interact with other social media users. Inactive user profiles will outnumber active ones. Platform providers will diversify into the WWW, e-commerce, edu-tech, fintechs, the automobile industry, and HR. They will change to a freemium business model and put more effort into combating cybercrime. Social media will become the predominant news distributor, but fake news will still be problematic. Firms will spend greater amounts of their budgets on social media advertising, and schools, politicians, and the medical sector will increase their social media engagement. Social media use will increasingly lead to individuals’ psychic issues. Society will benefit from economic growth and new jobs, increased political interest, democratic progress, and education due to social media. However, censorship and the energy consumption of platform operators might rise. KW - Delphi study KW - individual effects KW - interactive technologies KW - news media KW - social media KW - societal effects Y1 - 2020 U6 - https://doi.org/10.3390/fi12090146 SN - 1999-5903 VL - 12 IS - 9 PB - MDPI CY - Basel ER - TY - JOUR A1 - Skowronski, Marika A1 - Busching, Robert A1 - Krahé, Barbara T1 - Links between exposure to sexualized Instagram images and body image concerns in girls and boys JF - Journal of media psychology N2 - The current study examined the links between viewing female and male sexualized Instagram images (SII) and body image concerns within the three-step process of self-objectification among adolescents aged 13-18 years from Germany (N = 300, 61% female). Participants completed measures of SII use, thin- and muscular-ideal internalization, valuing appearance over competence, and body surveillance. Structural equation modeling revealed that SII use was associated with body image concerns for boys and girls via different routes. Specifically, female SII use was indirectly associated with higher body surveillance via thin-ideal internalization and subsequent valuing appearance over competence for girls. For both girls and boys, male SII use was indirectly linked to higher body surveillance via muscular-ideal internalization. Implications for the three-step model of self-objectification by sexualized social media are discussed. KW - social media KW - sexualization KW - body image concerns KW - self-objectification; KW - body surveillance Y1 - 2022 U6 - https://doi.org/10.1027/1864-1105/a000296 SN - 1864-1105 SN - 2151-2388 VL - 34 IS - 1 SP - 55 EP - 62 PB - Hogrefe & Huber Publ. [u.a.] CY - Göttingen ER - TY - JOUR A1 - Skowronski, Marika A1 - Busching, Robert A1 - Krahé, Barbara T1 - Predicting adolescents’ self-objectification from sexualized video game and Instagram use BT - A longitudinal study JF - Sex roles : a journal of research N2 - A growing body of research has demonstrated negative effects of sexualization in the media on adolescents' body image, but longitudinal studies and research including interactive and social media are scarce. The current study explored the longitudinal associations of adolescents' use of sexualized video games (SVG) and sexualized Instagram images (SII) with body image concerns. Specifically, our study examined relations between adolescents' SVG and SII use and appearance comparisons, thin- and muscular-ideal internalization, valuing appearance over competence, and body surveillance. A sample of 660 German adolescents (327 female, 333 male;M-age = 15.09 years) participated in two waves with an interval of 6 months. A structural equation model showed that SVG and SII use at Time 1 predicted body surveillance indirectly via valuing appearance over competence at Time 2. Furthermore, SVG and SII use indirectly predicted both thin- and muscular-ideal internalization through appearance comparisons at Time 1. In turn, thin-ideal internalization at Time 1 predicted body surveillance indirectly via valuing appearance over competence at Time 2. The results indicate that sexualization in video games and on Instagram can play an important role in increasing body image concerns among adolescents. We discuss the findings with respect to objectification theory and the predictive value of including appearance comparisons in models explaining the relation between sexualized media and self-objectification. KW - social media KW - computer games KW - sexualization KW - body image KW - self-objectification Y1 - 2020 U6 - https://doi.org/10.1007/s11199-020-01187-1 SN - 0360-0025 SN - 1573-2762 VL - 84 IS - 9-10 SP - 584 EP - 598 PB - Springer CY - New York ER - TY - GEN A1 - Skowronski, Marika A1 - Busching, Robert A1 - Krahé, Barbara T1 - Predicting adolescents’ self-objectification from sexualized video game and Instagram use BT - A longitudinal study T2 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe N2 - A growing body of research has demonstrated negative effects of sexualization in the media on adolescents' body image, but longitudinal studies and research including interactive and social media are scarce. The current study explored the longitudinal associations of adolescents' use of sexualized video games (SVG) and sexualized Instagram images (SII) with body image concerns. Specifically, our study examined relations between adolescents' SVG and SII use and appearance comparisons, thin- and muscular-ideal internalization, valuing appearance over competence, and body surveillance. A sample of 660 German adolescents (327 female, 333 male;M-age = 15.09 years) participated in two waves with an interval of 6 months. A structural equation model showed that SVG and SII use at Time 1 predicted body surveillance indirectly via valuing appearance over competence at Time 2. Furthermore, SVG and SII use indirectly predicted both thin- and muscular-ideal internalization through appearance comparisons at Time 1. In turn, thin-ideal internalization at Time 1 predicted body surveillance indirectly via valuing appearance over competence at Time 2. The results indicate that sexualization in video games and on Instagram can play an important role in increasing body image concerns among adolescents. We discuss the findings with respect to objectification theory and the predictive value of including appearance comparisons in models explaining the relation between sexualized media and self-objectification. T3 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 845 KW - social media KW - computer games KW - Sexualization KW - body image KW - self-objectification Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-541992 SN - 1866-8364 IS - 9-10 ER - TY - THES A1 - Sidarenka, Uladzimir T1 - Sentiment analysis of German Twitter T1 - Sentimentanalyse des deutschen Twitters N2 - The immense popularity of online communication services in the last decade has not only upended our lives (with news spreading like wildfire on the Web, presidents announcing their decisions on Twitter, and the outcome of political elections being determined on Facebook) but also dramatically increased the amount of data exchanged on these platforms. Therefore, if we wish to understand the needs of modern society better and want to protect it from new threats, we urgently need more robust, higher-quality natural language processing (NLP) applications that can recognize such necessities and menaces automatically, by analyzing uncensored texts. Unfortunately, most NLP programs today have been created for standard language, as we know it from newspapers, or, in the best case, adapted to the specifics of English social media. This thesis reduces the existing deficit by entering the new frontier of German online communication and addressing one of its most prolific forms—users’ conversations on Twitter. In particular, it explores the ways and means by how people express their opinions on this service, examines current approaches to automatic mining of these feelings, and proposes novel methods, which outperform state-of-the-art techniques. For this purpose, I introduce a new corpus of German tweets that have been manually annotated with sentiments, their targets and holders, as well as lexical polarity items and their contextual modifiers. Using these data, I explore four major areas of sentiment research: (i) generation of sentiment lexicons, (ii) fine-grained opinion mining, (iii) message-level polarity classification, and (iv) discourse-aware sentiment analysis. In the first task, I compare three popular groups of lexicon generation methods: dictionary-, corpus-, and word-embedding–based ones, finding that dictionary-based systems generally yield better polarity lists than the last two groups. Apart from this, I propose a linear projection algorithm, whose results surpass many existing automatically-generated lexicons. Afterwords, in the second task, I examine two common approaches to automatic prediction of sentiment spans, their sources, and targets: conditional random fields (CRFs) and recurrent neural networks, obtaining higher scores with the former model and improving these results even further by redefining the structure of CRF graphs. When dealing with message-level polarity classification, I juxtapose three major sentiment paradigms: lexicon-, machine-learning–, and deep-learning–based systems, and try to unite the first and last of these method groups by introducing a bidirectional neural network with lexicon-based attention. Finally, in order to make the new classifier aware of microblogs' discourse structure, I let it separately analyze the elementary discourse units of each tweet and infer the overall polarity of a message from the scores of its EDUs with the help of two new approaches: latent-marginalized CRFs and Recursive Dirichlet Process. N2 - Die enorme Popularität von Online-Kommunikationsdiensten in den letzten Jahrzehnten hat nicht unser Leben massiv geändert (sodass Nachrichten sich wie Fegefeuer übers Internet ausbreiten, Präsidenten ihre Entscheidungen auf Twitter ankündigen, und Ergebnisse politischer Wahlen auf Facebook entschieden werden) sondern auch zu einem dramatischen Anstieg der Datenmenge geführt, die über solche Plattformen ausgetauscht werden. Deswegen braucht man heutzutage dringend zuverlässige, qualitätvolle NLP-Programme, um neue gesellschaftliche Bedürfnisse und Risiken in unzensierten Nutzernachrichten automatisch erkennen und abschätzen zu können. Leider sind die meisten modernen NLP-Anwendungen entweder auf die Analyse der Standardsprache (wie wir sie aus Zeitungstexten kennen) ausgerichtet oder im besten Fall an die Spezifika englischer Social Media angepasst. Diese Dissertation reduziert den bestehenden Rückstand, indem sie das "Neuland" der deutschen Online-Kommunikation betritt und sich einer seiner produktivsten Formen zuwendet—den User-Diskussionen auf Twitter. Diese Arbeit erforscht insbesondere die Art und Weise, wie Leute ihre Meinungen auf diesem Online-Service äußern, analysiert existierende Verfahren zur automatischen Erkennung ihrer Gefühle und schlägt neue Verfahren vor, die viele heutige State-of-the-Art-Systeme übertreffen. Zu diesem Zweck stelle ich ein neues Korpus deutscher Tweets vor, die manuell von zwei menschlichen Experten mit Sentimenten (polaren Meinungen), ihren Quellen (sources) und Zielen (targets) sowie lexikalischen polaren Termen und deren kontextuellen Modifizierern annotiert wurden. Mithilfe dieser Daten untersuche ich vier große Teilgebiete der Sentimentanalyse: (i) automatische Generierung von Sentiment-Lexika, (ii) aspekt-basiertes Opinion-Mining, (iii) Klassifizierung der Polarität von ganzen Nachrichten und (iv) diskurs-bewusste Sentimentanalyse. In der ersten Aufgabe vergleiche ich drei populäre Gruppen von Lexikongenerierungsmethoden: wörterbuch-, corpus- und word-embedding-basierte Verfahren, und komme zu dem Schluss, dass wörterbuch-basierte Ansätze generell bessere Polaritätslexika liefern als die letzten zwei Gruppen. Abgesehen davon, schlage ich einen neuen Linearprojektionsalgorithmus vor, dessen Resultate deutlich besser als viele automatisch generierte Polaritätslisten sind. Weiterhin, in der zweiten Aufgabe, untersuche ich zwei gängige Herangehensweisen an die automatische Erkennung der Textspannen von Sentimenten, Sources und Targets: Conditional Random Fields (CRFs) und rekurrente neuronale Netzwerke. Ich erziele bessere Ergebnisse mit der ersten Methode und verbessere diese Werte noch weiter durch alternative Topologien der CRF-Graphen. Bei der Analyse der Nachrichtenpolarität stelle ich drei große Sentiment-Paradigmen gegenüber: lexikon-, Machine-Learning–, und Deep-Learning–basierte Systeme, und versuche die erste und die letzte dieser Gruppen in einem Verfahren zu vereinigen, indem ich eine neue neuronale Netzwerkarchitektur vorschlage: bidirektionales rekurrentes Netzwerk mit lexikon-basierter Attention (LBA). Im letzten Kapitel unternehme ich einen Versuch, die Prädiktion der Gesamtpolarität von Tweets über die Diskursstruktur der Nachrichten zu informieren. Zu diesem Zweck wende ich den vorgeschlagenen LBA-Klassifikator separat auf jede einzelne elementare Diskurs-Einheit (EDU) eines Microblogs an und induziere die allgemeine semantische Ausrichtung dieser Nachricht mithilfe von zwei neuen Methoden: latenten marginalisierten CRFs und rekursivem Dirichlet-Prozess. KW - sentiment analysis KW - opinion mining KW - social media KW - Twitter KW - natural language processing KW - discourse analysis KW - NLP KW - computational linguistics KW - machine learning KW - Sentimentanalyse KW - Computerlinguistik KW - Meinungsforschung Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-437422 ER - TY - THES A1 - Santos Bruss, Sara Morais dos T1 - Feminist solidarities after modulation N2 - Feminist Solidarities after Modulation produces an intersectional analysis of transnational feminist movements and their contemporary digital frameworks of identity and solidarity. Engaging media theory, critical race theory, and Black feminist theory, as well as contemporary feminist movements, this book argues that digital feminist interventions map themselves onto and make use of the multiplicity and ambiguity of digital spaces to question presentist and fixed notions of the internet as a white space and technologies in general as objective or universal. Understanding these frameworks as colonial constructions of the human, identity is traced to a socio-material condition that emerges with the modernity/colonialism binary. In the colonial moment, race and gender become the reasons for, as well as the effects of, technologies of identification, and thus need to be understood as and through technologies. What Deleuze has called modulation is not a present modality of control, but is placed into a longer genealogy of imperial division, which stands in opposition to feminist, queer, and anti-racist activism that insists on non-modular solidarities across seeming difference. At its heart, Feminist Solidarities after Modulation provides an analysis of contemporary digital feminist solidarities, which not only work at revealing the material histories and affective ""leakages"" of modular governance, but also challenges them to concentrate on forms of political togetherness that exceed a reductive or essentialist understanding of identity, solidarity, and difference. KW - social media KW - decolonial feminism KW - Germany KW - India KW - intersectionality KW - modulation KW - identity politics Y1 - 2020 SN - 978-1-68571-146-7 SN - 978-1-68571-147-4 U6 - https://doi.org/10.53288/0397.1.00 PB - punctum books CY - Brooklyn, NY ER - TY - CHAP A1 - Risius, Marten A1 - Baumann, Annika A1 - Krasnova, Hanna T1 - Developing a new paradigm BT - introducing the intention-behaviour gap to the privacy paradox phenomenon T2 - Proceedings of the 28th European Conference on Information Systems (ECIS) : ECIS 2020 Research Papers N2 - Internet users commonly agree that it is important for them to protect their personal data. However, the same users readily disclose their data when requested by an online service. The dichotomy between privacy attitude and actual behaviour is commonly referred to as the “privacy paradox”. Over twenty years of research were not able to provide one comprehensive explanation for the paradox and seems even further from providing actual means to overcome the paradox. We argue that the privacy paradox is not just an instantiation of the attitude-behaviour gap. Instead, we introduce a new paradigm explaining the paradox as the result of attitude-intention and intentionbehaviour gaps. Historically, motivational goal-setting psychologists addressed the issue of intentionbehaviour gaps in terms of the Rubicon Model of Action Phases and argued that commitment and volitional strength are an essential mechanism that fuel intentions and translate them into action. Thus, in this study we address the privacy paradox from a motivational psychological perspective by developing two interventions on Facebook and assess whether the 287 participants of our online experiment actually change their privacy behaviour. The results demonstrate the presence of an intentionbehaviour gap and the efficacy of our interventions in reducing the privacy paradox. KW - privacy paradox KW - intention-behaviour gap KW - attitude-behaviour gap KW - commitment KW - rubicon model KW - social media Y1 - 2020 UR - https://aisel.aisnet.org/ecis2020_rp/150 UR - https://www.researchgate.net/publication/341507497_Developing_a_New_Paradigm_Introducing_the_Intention-Behaviour_Gap_to_the_Privacy_Paradox_Phenomenon/link/5ec4a1c892851c11a8778d3f/download?_tp=eyJjb250ZXh0Ijp7InBhZ2UiOiJwdWJsaWNhdGlvbiIsInByZXZpb3VzUGFnZSI6bnVsbH19 PB - AIS Electronic Library (AISeL) CY - [Erscheinungsort nicht ermittelbar] ER - TY - THES A1 - Risch, Julian T1 - Reader comment analysis on online news platforms N2 - Comment sections of online news platforms are an essential space to express opinions and discuss political topics. However, the misuse by spammers, haters, and trolls raises doubts about whether the benefits justify the costs of the time-consuming content moderation. As a consequence, many platforms limited or even shut down comment sections completely. In this thesis, we present deep learning approaches for comment classification, recommendation, and prediction to foster respectful and engaging online discussions. The main focus is on two kinds of comments: toxic comments, which make readers leave a discussion, and engaging comments, which make readers join a discussion. First, we discourage and remove toxic comments, e.g., insults or threats. To this end, we present a semi-automatic comment moderation process, which is based on fine-grained text classification models and supports moderators. Our experiments demonstrate that data augmentation, transfer learning, and ensemble learning allow training robust classifiers even on small datasets. To establish trust in the machine-learned models, we reveal which input features are decisive for their output with attribution-based explanation methods. Second, we encourage and highlight engaging comments, e.g., serious questions or factual statements. We automatically identify the most engaging comments, so that readers need not scroll through thousands of comments to find them. The model training process builds on upvotes and replies as a measure of reader engagement. We also identify comments that address the article authors or are otherwise relevant to them to support interactions between journalists and their readership. Taking into account the readers' interests, we further provide personalized recommendations of discussions that align with their favored topics or involve frequent co-commenters. Our models outperform multiple baselines and recent related work in experiments on comment datasets from different platforms. N2 - Kommentarspalten von Online-Nachrichtenplattformen sind ein essentieller Ort, um Meinungen zu äußern und politische Themen zu diskutieren. Der Missbrauch durch Trolle und Verbreiter von Hass und Spam lässt jedoch Zweifel aufkommen, ob der Nutzen die Kosten der zeitaufwendigen Kommentarmoderation rechtfertigt. Als Konsequenz daraus haben viele Plattformen ihre Kommentarspalten eingeschränkt oder sogar ganz abgeschaltet. In dieser Arbeit stellen wir Deep-Learning-Verfahren zur Klassifizierung, Empfehlung und Vorhersage von Kommentaren vor, um respektvolle und anregende Online-Diskussionen zu fördern. Das Hauptaugenmerk liegt dabei auf zwei Arten von Kommentaren: toxische Kommentare, die die Leser veranlassen, eine Diskussion zu verlassen, und anregende Kommentare, die die Leser veranlassen, sich an einer Diskussion zu beteiligen. Im ersten Schritt identifizieren und entfernen wir toxische Kommentare, z.B. Beleidigungen oder Drohungen. Zu diesem Zweck stellen wir einen halbautomatischen Moderationsprozess vor, der auf feingranularen Textklassifikationsmodellen basiert und Moderatoren unterstützt. Unsere Experimente zeigen, dass Datenanreicherung, Transfer- und Ensemble-Lernen das Trainieren robuster Klassifikatoren selbst auf kleinen Datensätzen ermöglichen. Um Vertrauen in die maschinell gelernten Modelle zu schaffen, zeigen wir mit attributionsbasierten Erklärungsmethoden auf, welche Teile der Eingabe für ihre Ausgabe entscheidend sind. Im zweiten Schritt ermutigen und markieren wir anregende Kommentare, z.B. ernsthafte Fragen oder sachliche Aussagen. Wir identifizieren automatisch die anregendsten Kommentare, so dass die Leser nicht durch Tausende von Kommentaren blättern müssen, um sie zu finden. Der Trainingsprozess der Modelle baut auf Upvotes und Kommentarantworten als Maß für die Aktivität der Leser auf. Wir identifizieren außerdem Kommentare, die sich an die Artikelautoren richten oder anderweitig für sie relevant sind, um die Interaktion zwischen Journalisten und ihrer Leserschaft zu unterstützen. Unter Berücksichtigung der Interessen der Leser bieten wir darüber hinaus personalisierte Diskussionsempfehlungen an, die sich an den von ihnen bevorzugten Themen oder häufigen Diskussionspartnern orientieren. In Experimenten mit Kommentardatensätzen von verschiedenen Plattformen übertreffen unsere Modelle mehrere grundlegende Vergleichsverfahren und aktuelle verwandte Arbeiten. T2 - Analyse von Leserkommentaren auf Online-Nachrichtenplattformen KW - machine learning KW - Maschinelles Lernen KW - text classification KW - Textklassifikation KW - social media KW - Soziale Medien KW - hate speech detection KW - Hasserkennung Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-489222 ER - TY - GEN A1 - Pearce, Warren A1 - Özkula, Suay M. A1 - Greene, Amanda K. A1 - Teeling, Lauren A1 - Bansard, Jennifer S. A1 - Omena, Janna Joceli A1 - Rabello, Elaine Teixeira T1 - Visual cross-platform analysis BT - Digital methods to research social media images T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - Analysis of social media using digital methods is a flourishing approach. However, the relatively easy availability of data collected via platform application programming interfaces has arguably led to the predominance of single-platform research of social media. Such research has also privileged the role of text in social media analysis, as a form of data that is more readily gathered and searchable than images. In this paper, we challenge both of these prevailing forms of social media research by outlining a methodology for visual cross-platform analysis (VCPA), defined as the study of still and moving images across two or more social media platforms. Our argument contains three steps. First, we argue that cross-platform analysis addresses a gap in research methods in that it acknowledges the interplay between a social phenomenon under investigation and the medium within which it is being researched, thus illuminating the different affordances and cultures of web platforms. Second, we build on the literature on multimodal communication and platform vernacular to provide a rationale for incorporating the visual into cross-platform analysis. Third, we reflect on an experimental cross-platform analysis of images within social media posts (n = 471,033) used to communicate climate change to advance different modes of macro- and meso-levels of analysis that are natively visual: image-text networks, image plots and composite images. We conclude by assessing the research pathways opened up by VCPA, delineating potential contributions to empirical research and theory and the potential impact on practitioners of social media communication. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 199 KW - research methodology KW - visual analysis KW - social media KW - climate change Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-515539 SN - 1867-5808 IS - 2 ER - TY - JOUR A1 - Pearce, Warren A1 - Özkula, Suay M. A1 - Greene, Amanda K. A1 - Teeling, Lauren A1 - Bansard, Jennifer S. A1 - Omena, Janna Joceli A1 - Rabello, Elaine Teixeira T1 - Visual cross-platform analysis JF - Information, Communication and Society: digital methods to research social media images N2 - Analysis of social media using digital methods is a flourishing approach. However, the relatively easy availability of data collected via platform application programming interfaces has arguably led to the predominance of single-platform research of social media. Such research has also privileged the role of text in social media analysis, as a form of data that is more readily gathered and searchable than images. In this paper, we challenge both of these prevailing forms of social media research by outlining a methodology for visual cross-platform analysis (VCPA), defined as the study of still and moving images across two or more social media platforms. Our argument contains three steps. First, we argue that cross-platform analysis addresses a gap in research methods in that it acknowledges the interplay between a social phenomenon under investigation and the medium within which it is being researched, thus illuminating the different affordances and cultures of web platforms. Second, we build on the literature on multimodal communication and platform vernacular to provide a rationale for incorporating the visual into cross-platform analysis. Third, we reflect on an experimental cross-platform analysis of images within social media posts (n = 471,033) used to communicate climate change to advance different modes of macro- and meso-levels of analysis that are natively visual: image-text networks, image plots and composite images. We conclude by assessing the research pathways opened up by VCPA, delineating potential contributions to empirical research and theory and the potential impact on practitioners of social media communication. KW - research methodology KW - visual analysis KW - social media KW - climate change Y1 - 2018 U6 - https://doi.org/10.1080/1369118X.2018.1486871 SN - 1468-4462 SN - 1369-118X VL - 23 IS - 2 SP - 161 EP - 180 PB - Routledge CY - London ER - TY - JOUR A1 - Meier, Adrian A1 - Krause, Hannes-Vincent T1 - Does passive social media use harm well-being? BT - an adversarial review JF - Journal of media psychology N2 - Research into the effects of social media on well-being often distinguishes “active” and “passive” use, with passive use supposedly more harmful to well-being (i.e., the passive use hypothesis). Recently, several studies and reviews have begun to question this hypothesis and its conceptual basis, the active/passive dichotomy. As this dichotomy has become a staple of social media research but evidence challenging its validity is mounting, a comprehensive debate on its pros, cons, and potential future is needed. This adversarial review brings together two voices – one more supportive, and the other more critical – toward the active/passive model. In constructive dialogue, we summarize and contrast our two opposing positions: The first position argues that the active/passive dichotomy is a useful framework because it adequately describes how and why passive use is (more) harmful for well-being. The second position challenges the validity of the dichotomy and the passive use hypothesis specifically. Arguments are presented alongside (a) the empirical basis, (b) conceptualization, and (c) operationalization of active and passive use, with particular focus on the passive use hypothesis. Rather than offering a conciliatory summary of the status quo, the goal of this review is to carve out key points of friction in the literature on the effects of social media through fruitful debate. We summarize our main agreements and unresolved disagreements on the merits and shortcomings of the active/passive dichotomy. In doing so, this review paves the way for researchers to decide whether and how they want to continue applying this lens in their future work. KW - social media KW - active/passive dichotomy KW - well-being KW - adversarial review Y1 - 2022 U6 - https://doi.org/10.1027/1864-1105/a000358 SN - 1864-1105 SN - 2151-2388 VL - 35 IS - 3 SP - 169 EP - 180 PB - Hogrefe CY - Göttingen ER - TY - JOUR A1 - Krause, Hannes-Vincent A1 - Große Deters, Fenne A1 - Baumann, Annika A1 - Krasnova, Hanna T1 - Active social media use and its impact on well-being BT - an experimental study on the effects of posting pictures on Instagram JF - Journal of computer-mediated communication : a journal of the International Communication Association N2 - Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established hypothesis is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with considerable heterogeneity among existing studies on the hypothesis and causal evidence still limited, a final verdict on its robustness is still pending. To contribute to this ongoing debate, we conducted a week-long randomized control trial with N = 381 adult Instagram users recruited via Prolific. Specifically, we tested how active SNS use, operationalized as picture postings on Instagram, affects different dimensions of well-being. The results depicted a positive effect on users' positive affect but null findings for other well-being outcomes. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs.
Lay Summary Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established assumption is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with great diversity among conducted studies on the hypothesis and a lack of causal evidence, a final verdict on its viability is still pending. To contribute to this ongoing debate, we conducted a week-long experimental investigation with 381 adult Instagram users. Specifically, we tested how posting pictures on Instagram affects different aspects of well-being. The results of this study depicted a positive effect of posting Instagram pictures on users' experienced positive emotions but no effects on other aspects of well-being. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs on users. KW - social networking sites KW - social media KW - Instagram KW - well-being KW - experiment KW - randomized control trial Y1 - 2022 U6 - https://doi.org/10.1093/jcmc/zmac037 SN - 1083-6101 VL - 28 IS - 1 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Kapidzic, Sanja A1 - Frey, Felix A1 - Neuberger, Christoph A1 - Stieglitz, Stefan A1 - Mirbabaie, Milad T1 - Crisis communication on Twitter BT - differences between user types in top tweets about the 2015 “refugee crisis” in Germany JF - International journal of communication N2 - The study explores differences between three user types in the top tweets about the 2015 “refugee crisis” in Germany and presents the results of a quantitative content analysis. All tweets with the keyword “Flüchtlinge” posted for a monthlong period following September 13, 2015, the day Germany decided to implement border controls, were collected (N = 763,752). The top 2,495 tweets according to number of retweets were selected for analysis. Differences between news media, public and private actor tweets in topics, tweet characteristics such as tone and opinion expression, links, and specific sentiments toward refugees were analyzed. We found strong differences between the tweets. Public actor tweets were the main source of positive sentiment toward refugees and the main information source on refugee support. News media tweets mostly reflected traditional journalistic norms of impartiality and objectivity, whereas private actor tweets were more diverse in sentiments toward refugees. KW - refugee crisis 2015 KW - Germany KW - social media KW - Twitter KW - user types Y1 - 2023 UR - https://ijoc.org/index.php/ijoc/article/view/18172/4022 SN - 1932-8036 VL - 17 SP - 735 EP - 754 PB - The Annenberg Center for Communication CY - Los Angeles, Calif. ER - TY - JOUR A1 - Haugsbakken, Halvdan T1 - The Student Learning Ecology JF - KEYCIT 2014 - Key Competencies in Informatics and ICT N2 - Educational research on social media has showed that students use it for socialisation, personal communication, and informal learning. Recent studies have argued that students to some degree use social media to carry out formal schoolwork. This article gives an explorative account on how a small sample of Norwegian high school students use social media to self-organise formal schoolwork. This user pattern can be called a “student learning ecology”, which is a user perspective on how participating students gain access to learning resources. KW - Learning ecology KW - social media KW - high school KW - Norway Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-82659 SN - 1868-0844 SN - 2191-1940 IS - 7 SP - 151 EP - 169 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - THES A1 - Hannes-Vincent, Krause T1 - Social networking site use and well-being - a nuanced understanding of a complex relationship N2 - Social Networking Sites (SNSs) are ubiquitous and attract an enormous chair of the digital population. Their functionalities allow users to connect and interact with others and weave complex social networks in which social information is continuously disseminated between users. Besides the social value SNSs are generating, they likewise attract companies and allow for new forms of marketing, thereby creating considerable economic value alike. However, as SNSs grew in popularity, so did concerns about the impact of their use on social interactions in general and the well-being of individual users in particular. While existing scientific evidence points to both risk as well as benefits of SNS use, research still lacks a profound understanding of which aspects of SNSs enable an impact on well-being and which psychological processes on the part of the users underly and explain this relationship. Therefore, this thesis is dedicated to an in-depth exploration of the relationship between SNS use and well-being and aims to answer how SNS use can impact well-being. Primarily, it focuses on the unique technological features that characterize SNSs and enable potential well- being alterations and on specific psychological processes on the part of the users, underlying and explaining the relationship. For this purpose, the thesis first introduces the concept of well- being. It continues by presenting SNSs’ unique technological features, divided into specifics of the content disseminated on SNSs and the network structure of SNSs. Further, the thesis introduces three classes of psychological processes assumed most relevant for the relationship between SNSs and well-being: other-focused, self-focused, and contrastive processes.. It is assumed that the course and quality of these common processes change in the SNS context and that a complex interplay between the unique features of SNSs and these processes determines how SNSs may ultimately affect users' well-being - both in positive and negative ways. The dissertation comprises seven research articles, each of which focusses on a particular set of SNS characteristics, their interplay with one or more of the proposed psychological processes, and ultimately the resulting effects on user well-being or its key resilience and risk factors. The seven articles investigate this relationship using different methodological approaches. Three articles are based on either systematic or narrative literature reviews, one applies an empirical cross-sectional research design, and three articles present an experimental investigation. Thematically, two articles revolve around SNS use’s effect on self-esteem. Three articles examine the specific role of the emotion of envy and its potential to establish and perpetuate a well-being-damaging social climate on SNSs. The two last articles of this thesis revolve around the established assumption that active and passive SNS use, as different modalities of SNS use, cause differential effects on users’ well-being due to the involvement of different psychological processes. The results of this thesis illustrate different ways how SNSs can affect users’ well-being. The results suggest that especially contrastive processes play a decisive role in explaining potential well-being risks for SNS users. Their interplay with certain SNS features seems to foster upward social comparisons and feelings of envy, potentially leading to a complex set of deleterious effects on users’ well-being. At the same time, the findings illuminate ways in which SNSs can benefit users and their self-esteem – especially when SNS use promotes self- focused and social-feedback-based other-focused processes. The thesis and their findings illustrate that the relationship between SNSs and well-being is complex. Therefore, a nuanced perspective, taking into consideration both the technological uniqueness of SNSs and the psychological processes they are enabling, is crucial to understand how these technologies affect their users in good and potentially harmful ways. On the one hand, the gathered insights contribute to research, providing novel insights into the complex relationship between SNS use and well-being. On the other hand, the results enable a focused and action-oriented derivation of recommendations for stakeholders such as individual users, policymakers, and platform providers. The findings of this thesis can help them to better combat SNS-related risks and ultimately ensure a healthy and sustainable environment for users - and thus also the economic values of SNSs - in the long term. KW - social networking sites KW - well-being KW - social media KW - self-esteem KW - envy Y1 - 2022 ER - TY - JOUR A1 - Hagemann, Linus A1 - Abramova, Olga T1 - Sentiment, we-talk and engagement on social media BT - insights from Twitter data mining on the US presidential elections 2020 JF - Internet research N2 - Purpose Given inconsistent results in prior studies, this paper applies the dual process theory to investigate what social media messages yield audience engagement during a political event. It tests how affective cues (emotional valence, intensity and collective self-representation) and cognitive cues (insight, causation, certainty and discrepancy) contribute to public engagement. Design/methodology/approach The authors created a dataset of more than three million tweets during the 2020 United States (US) presidential elections. Affective and cognitive cues were assessed via sentiment analysis. The hypotheses were tested in negative binomial regressions. The authors also scrutinized a subsample of far-famed Twitter users. The final dataset, scraping code, preprocessing and analysis are available in an open repository. Findings The authors found the prominence of both affective and cognitive cues. For the overall sample, negativity bias was registered, and the tweet’s emotionality was negatively related to engagement. In contrast, in the sub-sample of tweets from famous users, emotionally charged content produced higher engagement. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with many followers. Collective self-representation (“we-talk”) is consistently associated with more likes, comments and retweets in the overall sample and subsamples. Originality/value The authors expand the dominating one-sided perspective to social media message processing focused on the peripheral route and hence affective cues. Leaning on the dual process theory, the authors shed light on the effectiveness of both affective (peripheral route) and cognitive (central route) cues on information appeal and dissemination on Twitter during a political event. The popularity of the tweet’s author moderates these relationships. KW - social media KW - engagement KW - data mining KW - big data Y1 - 2023 U6 - https://doi.org/10.1108/INTR-12-2021-0885 SN - 1066-2243 VL - 33 IS - 6 SP - 2058 EP - 2085 PB - Emeral CY - Bingley ER - TY - CHAP A1 - Hagemann, Linus A1 - Abramova, Olga T1 - Crafting audience engagement in social media conversations BT - evidence from the U.S. 2020 presidential elections T2 - Proceedings of the 55th Hawaii International Conference on System Sciences N2 - Observing inconsistent results in prior studies, this paper applies the elaboration likelihood model to investigate the impact of affective and cognitive cues embedded in social media messages on audience engagement during a political event. Leveraging a rich dataset in the context of the 2020 U.S. presidential elections containing more than 3 million tweets, we found the prominence of both cue types. For the overall sample, positivity and sentiment are negatively related to engagement. In contrast, the post-hoc sub-sample analysis of tweets from famous users shows that emotionally charged content is more engaging. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with a vast number of followers. Prosocial orientation (“we-talk”) is consistently associated with more likes, comments, and retweets in the overall sample and sub-samples. KW - mediated conversation KW - big data KW - engagement KW - sentiment analysis KW - social media Y1 - 2022 SN - 978-0-9981331-5-7 SP - 3222 EP - 3231 PB - HICSS Conference Office University of Hawaii at Manoa CY - Honolulu ER - TY - JOUR A1 - Gabowitsch, Mischa T1 - Belarusian protest BT - regimes of engagement and coordination JF - Slavic review : interdisciplinary quarterly of Russian, Eurasian and East European studies / publ. by the Association for Slavic, East European, and Eurasian Studies N2 - The Belarusian protest movement that started in August 2020 has been discussed from the point of view of strategy and objectives, and as the cradle of a new subjectivity. This essay goes beyond those two perspectives by looking at the regimes of engagement, developing in interaction with the material and technological environment, that have given the protests their distinctive style. The first part looks at coordination and representation at protest events and in producing protest symbols such as flags. The second part discusses the role of Telegram and the emergence of local protest groups. Even though the movement did not grow organically out of everyday concerns, there are some signs that it has begun to reassemble local communities from above. Yet there are also indications that politics continues to be seen as distinct from everyday life, making it uncertain that the movement will lead to a deeper transformation of society. KW - Belarus KW - protest KW - regimes of engagement KW - flag-making KW - social media Y1 - 2021 U6 - https://doi.org/10.1017/slr.2021.28 SN - 0037-6779 SN - 2325-7784 N1 - Critical Discussion Forum: The sociology of protest in Belarus — Social dynamics, ideological shifts, and demand for change VL - 80 IS - 1 SP - 27 EP - 37 PB - Cambridge University Press CY - Cambridge ER - TY - JOUR A1 - Fischer-Preßler, Diana A1 - Marx, Julian A1 - Bunker, Deborah A1 - Stieglitz, Stefan A1 - Fischbach, Kai T1 - Social media information governance in multi-level organizations BT - how humanitarian organizations accrue social capital JF - Information and management N2 - Strategic social media use positively influences organizational goals such as the long-term accrual of social capital, and thus social media information governance has become an increasingly important organizational objective. It is particularly important for humanitarian nongovernmental organizations (HNGOs), whose work relies on accurate and timely information regarding socially altruistic behavior (donations, volunteerism, etc.). Despite the potential of social media for increasing social capital, tensions in governing social media information across an organization's different operational levels (regional, intermediate, and national) pose a difficult challenge. Prominent governance frameworks offer little guidance, as their focus on control and incremental policymaking is largely incompatible with the processes, roles, standards, and metrics needed for managing self-governing social media. This study offers a notion of dynamic and co-evolutionary process management of multi-level organizations as a means of conceptualizing social media information governance for the accrual of organizational social capital. Based on interviews with members of HNGOs, this study reveals tensions that emerge within eight focus areas of accruing social capital in multi-level organizations, explains how dynamic process management can ease those tensions, and proposes corresponding strategy recommendations. KW - social media KW - social capital KW - information governance KW - dynamic and co-evolutionary process management Y1 - 2023 U6 - https://doi.org/10.1016/j.im.2023.103838 SN - 0378-7206 SN - 1872-7530 VL - 60 IS - 7 SP - 1 EP - 18 PB - Elsevier Science CY - Amsterdam ER - TY - THES A1 - Bin Tareaf, Raad T1 - Social media based personality prediction models T1 - Social Media-basierte Persönlichkeitsvorhersage Modelle N2 - Individuals have an intrinsic need to express themselves to other humans within a given community by sharing their experiences, thoughts, actions, and opinions. As a means, they mostly prefer to use modern online social media platforms such as Twitter, Facebook, personal blogs, and Reddit. Users of these social networks interact by drafting their own statuses updates, publishing photos, and giving likes leaving a considerable amount of data behind them to be analyzed. Researchers recently started exploring the shared social media data to understand online users better and predict their Big five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. This thesis intends to investigate the possible relationship between users’ Big five personality traits and the published information on their social media profiles. Facebook public data such as linguistic status updates, meta-data of likes objects, profile pictures, emotions, or reactions records were adopted to address the proposed research questions. Several machine learning predictions models were constructed with various experiments to utilize the engineered features correlated with the Big 5 Personality traits. The final predictive performances improved the prediction accuracy compared to state-of-the-art approaches, and the models were evaluated based on established benchmarks in the domain. The research experiments were implemented while ethical and privacy points were concerned. Furthermore, the research aims to raise awareness about privacy between social media users and show what third parties can reveal about users’ private traits from what they share and act on different social networking platforms. In the second part of the thesis, the variation in personality development is studied within a cross-platform environment such as Facebook and Twitter platforms. The constructed personality profiles in these social platforms are compared to evaluate the effect of the used platforms on one user’s personality development. Likewise, personality continuity and stability analysis are performed using two social media platforms samples. The implemented experiments are based on ten-year longitudinal samples aiming to understand users’ long-term personality development and further unlock the potential of cooperation between psychologists and data scientists. N2 - Menschen haben das Bedürfnis, sich anderen Menschen innerhalb einer bestimmten Gemeinschaft mitzuteilen, indem sie ihre Erfahrungen, Gedanken, Handlungen und Meinungen teilen. Zu diesem Zweck nutzen sie am liebsten moderne Online-Plattformen für soziale Medien wie Twitter, Facebook, persönliche Blogs und Reddit. Die Nutzer dieser sozialen Netzwerke interagieren, indem sie ihre eigenen Status-Updates verfassen, Fotos veröffentlichen und Likes vergeben und dabei eine beträchtliche Menge an Daten hinterlassen, die analysiert werden können. Forscher haben vor kurzem damit begonnen, die in den sozialen Medien geteilten Daten zu untersuchen, um die Online-Nutzer besser zu verstehen und ihre Big-Five-Persönlichkeitseigenschaften vorherzusagen: Verträglichkeit, Gewissenhaftigkeit, Extraversion, Neurotizismus und Offenheit für Erfahrungen. In dieser Arbeit soll der mögliche Zusammenhang zwischen den Big Five Persönlichkeitsmerkmalen der Nutzer und den in ihren Social-Media-Profilen veröffentlichten Informationen untersucht werden. Öffentliche Facebook-Daten wie sprachliche Status-Updates, Metadaten von Likes, Profilbilder, Emotionen oder Reaktionsaufzeichnungen wurden zur Beantwortung der vorgeschlagenen Forschungsfragen herangezogen. Es wurden mehrere Modelle des maschinellen Lernens mit verschiedenen Experimenten erstellt, um die entwickelten Merkmale zu nutzen, die mit den Big 5 Persönlichkeitsmerkmalen korrelieren. Die endgültigen Vorhersageleistungen verbesserten die Vorhersagegenauigkeit im Vergleich zu modernsten Ansätzen, und die Modelle wurden auf der Grundlage etablierter Benchmarks in diesem Bereich bewertet. Die Forschungsexperimente wurden unter Berücksichtigung ethischer Aspekte und des Datenschutzes durchgeführt. Darüber hinaus zielt die Forschung darauf ab, das Bewusstsein für die Privatsphäre von Nutzern sozialer Medien zu schärfen und zu zeigen, was Dritte über die privaten Eigenschaften von Nutzern aus dem, was sie auf verschiedenen sozialen Netzwerkplattformen teilen und tun, herausfinden können. Im zweiten Teil der Arbeit werden die Unterschiede in der Persönlichkeitsentwicklung in einer plattformübergreifenden Umgebung wie Facebook und Twitter untersucht. Die konstruierten Persönlichkeitsprofile in diesen sozialen Plattformen werden verglichen, um die Auswirkungen der verwendeten Plattformen auf die Persönlichkeitsentwicklung eines Nutzers zu bewerten. Ebenso werden Persönlichkeitskontinuität und -stabilität anhand von zwei Social Media Plattformen untersucht. Die durchgeführten Experimente basieren auf zehnjährigen Längsschnittstichproben mit dem Ziel, die langfristige Persönlichkeitsentwicklung der Nutzer zu verstehen und das Potenzial der Zusammenarbeit zwischen Psychologen und Datenwissenschaftlern weiter zu erschließen. KW - social media KW - online personality KW - social networking KW - Online-Persönlichkeit KW - sozialen Medien KW - soziales Netzwerk Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-549142 ER - TY - JOUR A1 - Abramova, Olga A1 - Batzel, Katharina A1 - Modesti, Daniela T1 - Collective response to the health crisis among German Twitter users BT - a structural topic modeling approach JF - International Journal of Information Management Data Insights N2 - We used structural topic modeling to analyze over 800,000 German tweets about COVID-19 to answer the questions: What patterns emerge in tweets as a response to a health crisis? And how do topics discussed change over time? The study leans on the goals associated with the health information seeking (GAINS) model, discerning whether a post aims at tackling and eliminating the problem (i.e., problem-focused) or managing the emotions (i.e., emotion-focused); whether it strives to maximize positive outcomes (promotion focus) or to minimize negative outcomes (prevention focus). The findings indicate four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Public communication is volatile over time, and a shift is evidenced from self-centered to community-centered topics within 4.5 weeks. Our study illustrates social media text mining's potential to quickly and efficiently extract public opinions and reactions. Monitoring fears and trending topics enable policymakers to rapidly respond to deviant behavior, like resistive attitudes toward containment measures or deteriorating physical health. Healthcare workers can use the insights to provide mental health services for battling anxiety or extensive loneliness from staying home. KW - social media KW - Twitter KW - modeling KW - regulatory focus theory KW - crisis management KW - text mining Y1 - 2022 U6 - https://doi.org/10.1016/j.jjimei.2022.100126 SN - 2667-0968 VL - 2 IS - 2 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Abramova, Olga A1 - Batzel, Katharina A1 - Modesti, Daniela T1 - Coping and regulatory responses on social media during health crisis BT - a large-scale analysis T2 - Proceedings of the 55th Hawaii International Conference on System Sciences N2 - During a crisis event, social media enables two-way communication and many-to-many information broadcasting, browsing others’ posts, publishing own content, and public commenting. These records can deliver valuable insights to approach problematic situations effectively. Our study explores how social media communication can be analyzed to understand the responses to health crises better. Results based on nearly 800 K tweets indicate that the coping and regulation foci framework holds good explanatory power, with four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Second, the inter-temporal analysis shows high volatility of topic proportions and a shift from self-centered to community-centered topics during the course of the event. The insights are beneficial for research on crisis management and practicians who are interested in large-scale monitoring of their audience for well-informed decision-making. KW - Digital-Enabled Human-Information Interaction KW - big data KW - data mining KW - health crisis KW - social media Y1 - 2022 SN - 978-0-9981331-5-7 PB - HICSS Conference Office University of Hawaii at Manoa CY - Honolulu ER -