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 - CHAP A1 - Stieglitz, Stefan A1 - Fromm, Jennifer A1 - Kocur, Alexander A1 - Rostalski, Frauke A1 - Duda, Michelle A1 - Evans, Alison A1 - Rieskamp, Jonas A1 - Sievi, Luzia A1 - Pawelec, Maria A1 - Heesen, Jessica A1 - Loh, Wulf A1 - Fuchß, Christoph A1 - Eyilmez, Kaan T1 - What measures can government institutions in Germany take against digital disinformation? BT - a systematic literature review and ethical-legal discussion T2 - Wirtschaftsinformatik 2023 Proceedings N2 - Disinformation campaigns spread rapidly through social media and can cause serious harm, especially in crisis situations, ranging from confusion about how to act to a loss of trust in government institutions. Therefore, the prevention of digital disinformation campaigns represents an important research topic. However, previous research in the field of information systems focused on the technical possibilities to detect and combat disinformation, while ethical and legal perspectives have been neglected so far. In this article, we synthesize previous information systems literature on disinformation prevention measures and discuss these measures from an ethical and legal perspective. We conclude by proposing questions for future research on the prevention of disinformation campaigns from an IS, ethical, and legal perspective. In doing so, we contribute to a balanced discussion on the prevention of digital disinformation campaigns that equally considers technical, ethical, and legal issues, and encourage increased interdisciplinary collaboration in future research. KW - disinformation campaigns KW - social media KW - ethical implications KW - legal implications KW - government agencies Y1 - 2023 UR - https://aisel.aisnet.org/wi2023/20/ PB - AIS Electronic Library (AISeL) CY - [Erscheinungsort nicht ermittelbar] 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 - 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 - 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 - 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 -