Topics for bachelor / master theses & media projects
iStockphoto / Frank Peters
All topics offered here can be implemented in exactly the form presented here, but of course also in a modified form in a thesis. Talk to a person in the field or your supervisor about whether you have your own idea that you want to tackle, whether you want to implement a topic as presented or develop something of your own with the help of the supervisor, which is only roughly based on the topics presented. In principle, many things are possible, just ask!
The involvement of citizens in socio-political processes is generally perceived and described as desirable. Citizen participation is particularly relevant in the transformative efforts of cities and local governments to become smart cities. More than 50% of the world's population lives in cities, and it is estimated that this proportion will rise to more than two-thirds by 2050. Cities are spaces where existing social divides are widening, but they are also sources of inspiration for a new wave of thinking within local publics, focusing in particular on combining technological and social innovation to address the current problems of urbanisation, with the central aim of improving public wellbeing. This raises the question of the extent to which this citizen participation contributes to improving information, communication and participation processes in a democracy in general and in cities in particular. Thanks to the various forms of Web2.0 online communication (e.g. YouTube, Facebook, blogs), citizens have various opportunities to disseminate their content and communication messages, as well as those responsible in cities or city-related institutions.
As part of a media project, various questions can be addressed, such as
What communication channels do city officials use?
How are these communication channels used?
If you are interested in this topic, please contact Max Schindler.
In this media project, students should independently develop a podcast format for a selected topic from a media and communication science perspective. In addition to creating a concept, the first content (e.g. episodes) will be developed and put into practice. A strategy for the distribution of the podcast and corresponding measures for external communication complete the portfolio.
If you are interested in this topic, please contact Prof. Dr. Emese Domahidi.
We generate more data today than ever before. Think about how many posts and comments you have made on Facebook since you joined. Or how many videos have been posted on YouTube or TikTok since these platforms were launched. Our social media communications are not the only form of digital data. Many governments, for example, choose to make the data they produce available to their citizens. This can include parliamentary records (e.g. politicians' speeches) or proposed legislation. In this media project, you will create a directory of such open source datasets containing communication data with a focus on the European Union in general and/or Germany in particular. This can include text, images, audio and video data that capture human communication. You can start this project by looking at the results of a report on open government data produced by the OECD (https://www.oecd.org/digital/digital-government/open-government-data.htm). The project is not limited to data provided by governments, but can/should also explore other sources, such as open source datasets provided by non-governmental organisations, private/public companies, research institutions, etc.
If you are interested, please contact Aliya Andrich.
To understand right-wing protests and the right-wing mobilisation behind them, it is necessary to examine not only the protesters but also the actors who mobilise them. Right-wing actors follow recurring patterns when it comes to building networks and articulating issues. Here, right-wing actors rely primarily on new (social) media platforms (e.g. Telegram) to build network structures, which are then used to link current crises (e.g. COVID-19) with old ideological frames such as racism, anti-Semitism and anti-modernism (Caiani et al., 2012).
The media project focuses on the systematic creation of a register of current right-wing actors and their associated social media channels. In addition to classic platforms such as Facebook, Twitter and YouTube, the focus will be on alternative fringe platforms such as Telegram, Parler, Gab, Gettr and Odysee, and the systematic approach is important for the creation of the register, as this register will be used as a basis for data collection in future studies.
If you are interested, please contact Maximilian Zehring.
Reference:
Caiani, M., Della Porta, D., & Wagemann, C. (2012). Mobilizing on the extreme right: Germany, Italy, and the United States. Oxford University Press.
In general, both BA and MA theses are possible at our department if you want to research the following topics: Digital communication, social media and the effects of its use, gender bias, smart cities / urban communication, right-wing extremism and conspiracy theories, well being / mental health, political communication, election studies, democratic participation in the digital age, gender and/or racial inequalities. All manual and/or automated methodological approaches of communication science are conceivable.
Other topics are of course also possible; if you are interested, simply contact ccs-wm@tu-ilmenau.de.
Seit einiger Zeit stehen etablierte Medien heftig in der Kritik aufgrund einer vermeintlich falschen Berichterstattung über Themen wie die „Flüchtlingskrise“ (Krüger & Seiffert-Brockmann, 2017). Der Begriff Lügenpresse steht für diese Vorwürfe und wurde in den letzten Jahren vor allem durch die „Pegida“ Bewegung wieder stark in das öffentliche Bewusstsein gerückt. Während erste Studien untersuchen wie klassische Medien die Vorwürfe thematisieren (Denner & Peter, 2017), oder inwiefern diese Vorwürfe über eine falsche oder einseitige Berichterstattung haltbar sind (Maurer, Jost, Haßler, & Kruschinski, 2019), ist über die Diskussion des Begriffes Lügenpresse in YouTube relativ wenig bekannt (Holt & Haller, 2017). Soziale Medien spielen aber eine herausragende Rolle, auch für die Diskussionen von Themen und Meinungen die vermeintlich nicht auf der Medienagenda stehen. Die Arbeit untersucht die Thematik anhand einer manuellen Inhaltsanalyse der Videos zu dem Vorwurf „Lügenpresse“ in YouTube.
Denner, N., & Peter, C. (2017). Der Begriff Lügenpresse in deutschen Tageszeitungen: Eine Framing-Analyse. Publizistik, 62(3), 273-297.
Holt, K., & Haller, A. (2017). What does ‘Lügenpresse’ mean? Expressions of media distrust on PEGIDA’s Facebook pages. Politik, 20(4).
Krüger, U., & Seiffert-Brockmann, J. (2018). „Lügenpresse “ – Eine Verschwörungstheorie? In H. Haarkötter & J.-U. Nieland (eds.), Nachrichten und Aufklärung. Medien- und Journalismuskritik heute: 20 Jahre Initiative Nachrichtenaufklärung (p. 67-87). Wiesbaden: VS Springer.
Maurer, M., Jost, P., Haßler, J., & Kruschinski, S. (2019). Auf den Spuren der Lügenpresse. Zur Richtigkeit und Ausgewogenheit der Medienberichterstattung in der „Flüchtlingskrise“. Publizistik, 64(1), 15-35.
Der erhöhte Bedarf an Informationen und Unterstützung in digitalisierten Arbeitswelten (WEF, 2016) wird u. a. durch die Nutzung von sozialen Online-Medien (OM) gedeckt, einerseits innerhalb der Organisationen, bspw. durch so genannte Enterprise Social Media (Leonardi, Huysman, & Steinfield, 2013), andererseits außerhalb der Organisationsgrenzen in OM die sowohl beruflich (z.B. LinkedIn, Stack Overflow) als auch allgemein (z. B. Facebook, Twitter, Youtube) orientiert sein können (Utz, 2015). Hierbei spielen auch insbesondere interessengeleitete Online-Gemeinschaften wie Foren oder Frage- und Antwort-Seiten eine herausragende Rolle, da ihre Nutzung eine positive Auswirkung auf die sozialen Ressourcen der Nutzenden hat (Domahidi, 2016) und die Qualität der Informationen durchaus hoch sein kann (Glogowska, Csaki, Feller, & Gleasure, 2016). Die Kommunikationswissenschaft beschäftigt sich bislang nur randständig mit diesem Themenbereich. In der Arbeit wird einen systematische Literaturübersicht zu dem Thema im kommunikationswissenschaftlichen Kontext erstellt.
Domahidi, E. (2016). Online-Mediennutzung und wahrgenommene soziale Ressourcen. Eine Metaanalyse. Wiesbaden: VS Springer.
Glogowska, D., Csáki, C., Feller, J., & Gleasure, R. (2016). Reputation, User Feedback, and Perceived Information Quality in Social Internet Media: An Empirical Study. In Proceedings of the Thirty Seventh International Conference on Information Systems (p. 28). Association for Information Systems. AIS Electronic Library (AISeL).
Leonardi, P. M., Huysman, M., & Steinfield, C. (2013). Enterprise social media: Definition, history, and prospects for the study of social technologies in organizations. Journal of Computer-Mediated Communication, 19(1), 1-19.
Utz, S. (2015). Is LinkedIn making you more successful? The informational benefits derived from public social media. New Media & Society, 18(11), 2685-2702.
World Economic Forum (WEF) (2016). The future of jobs. Employment, skills and workforce strategy for the fourth industrial revolution. Abgerufen von Global Challenge Insight Report: www3.weforum.org/docs/Media/WEF_FutureofJobs.pdf
Diskursqualität, vor allem in Nutzerkommentaren auf Nachrichtenseiten und Social Media, stellt ein hochrelevantes Thema dar und ist viel beforscht. In Ihrer Arbeit erstellen Sie eine Übersicht über aktuelle Literatur zu Diskursqualität in verschiedenen Online-Kontexten und mögliche Auswirkung dieser auf die mentale Gesundheit der beteiligten Personen.
De Choudhury, M., & De, S. (2014, May). Mental health discourse on reddit: Self-disclosure, social support, and anonymity. In Eighth international AAAI conference on weblogs and social media.
Mendu, S., Baglione, A., Baee, S., Wu, C., Ng, B., Shaked, A., ... & Barnes, L. (2020). A Framework for Understanding the Relationship between Social Media Discourse and Mental Health. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1-23.
Seering, J., Fang, T., Damasco, L., Chen, M. C., Sun, L., & Kaufman, G. (2019, May). Designing User Interface Elements to Improve the Quality and Civility of Discourse in Online Commenting Behaviors. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
Studierende sind herzlich dazu eingeladen Studien in den genannten Bereichen vorzuschlagen oder gemeinsam mit dem Betreuer zu finden und im Rahmen der Bachelor-/Masterarbeit zu replizieren. Eine Replikation muss (und wird) dabei keine 100% deckungsgleiche Durchführung der gewählten Studie sein. Vielmehr wird im Austausch mit dem Betreuer ein geeigneter Rahmen für die Bachelor-/Masterarbeit festgelegt.
Ein beispielhaftes Vorgehen für die Studie Multilevel Emotion Transfer on YouTube:Disentangling the Effects of EmotionalContagion and Homophily on Video Audiencesvon Rosenbusch und Kollegen (2019) könnte sich dabei aus folgenden Schritten zusammensetzen:
Bisherige Untersuchungen zeigen das auch heute noch, 30 Jahre nach der Wiedervereinigung, messbare sozioökonomische Unterschiede und altbekannte Stereotype zwischen Ost- und Westdeutschland bestehen (vgl. Foroutan et al., 2019). Ältere Paper weisen darauf hin, dass die traditionellen Medien mit ihrer Art und Weise der Berichterstattung über Ostdeutschland existierende Stereotype möglicherweise bestärken, während evidente soziöknomische Nachteile oft als ungerechtfertigte subjektive Gefühle der Ostdeutschen abgetan werden (z.B. „Jammerossi“, vgl. Kollmorgen & Hans, 2011; Foroutan et al., 2019; Hoff & Kausch, 2013). Im Gegensatz dazu wissen wir sehr wenig darüber, in welchem Ausmaß Stereotype gegenüber Ostdeutschen auf Social Media oder in anderen Online Medien anzutreffen sind.
Die zu verfassende Arbeit kann hierbei zwei mögliche Wege gehen: entweder wird versucht bisherige Ergebnisse zu replizieren indem sich die quantitative Inhaltsanalyse auf die traditionellen Medien bezieht, oder es wird eine explorative Studie durchgeführt welche mittels quantitativer Inhaltsanalyse Daten von Social Media oder anderen Online Medien untersucht.
Foroutan, N., Kalter, F., Canan, C., & Simon, M. (2019). Ost-Migrantische Analogien I - Konkurrenz um Anerkennung. Deutsches Zentrum für Integrations- und Migrationsforschung. www.dezim-institut.de/fileadmin/user_upload/Projekte/Ost-Migrantische_Analogien/OstMig_Booklet_A4.pdf
Hoff, I., & Kausch, S. (2013). Die neue innerdeutsche Grenze - Deutschland als Zwei-(Normalitäts-) Klassen-Gesellschaft. In R. Pates & M. Schochow (Eds.), Der „Ossi“ - Mikropolitische Studien über einen symbolischen Ausländer (pp. 83-103). Springer VS. doi.org/10.1007/978-3-531-94120-2
Kollmorgen, R., & Hans, T. (2011). Der verlorene Osten. Massenmediale Diskurse über Ostdeutschland und die deutsche Einheit. In R. Kollmorgen, F. T. Koch, & H.-L. Dienel (Eds.), Diskurse der deutschen Einheit. Kritik und Alternativen (pp. 107-165). VS Verlag für Sozialwissenschaften. doi.org/10.1007/978-3-531-93351-1_4
In dieser Arbeit soll mittels einer systematischen Literaturübersicht herausgefunden werden, wie und mit welchen Ergebnissen computationale Methoden (siehe Iliev et al., 2014; van Atteveldt & Peng, 2018) bei der Erforschung von Verschwörungstheorien in sozialen und online Medien eingesetzt werden. Bspw. können mithilfe dieser Methoden narrative Motive von Verschwörungstheorien herausgearbeitet werden (Samory & Tanushree, 2018) oder Aussagen darüber getroffen werden, welche Eigenschaften Verschwörungstheoretiker aufweisen (Enders & Smallpage, 2019). Die zu erarbeitende Übersicht fokussiert sich dabei auf eine detaillierte Beschreibung der Methoden und Ergebnisse solcher Untersuchungen. Die aufgeführten Referenzen können gerne als Startpunkt für die Arbeit verwendet werden.
Enders, A. M., & Smallpage, S. M. (2019). Who Are Conspiracy Theorists? A Comprehensive Approach to Explaining Conspiracy Beliefs. Social Science Quarterly, 100(6), 2017-2032. doi.org/10.1111/ssqu.12711
Iliev, R., Dehghani, M., & Sagi, E. (2014). Automated text analysis in psychology: methods, applications, and future developments. Language and Cognition, 7(2015), 265-290. doi.org/10.1017/langcog.2014.30
Samory, M., & Tanushree, M. (2018). „The Government Spies Using Our Webcams“: The Language of Conspiracy Theories in Online Discussion. Proceedings of the ACM on Human-Computer Interaction, 2, 1-24. doi.org/10.1145/3274421
van Atteveldt, W., & Peng, T.-Q. (2018). When Communication Meets Computation: Opportunities, Challenges, and Pitfalls in Computational Communication Science. Communication Methods and Measures, 12(2-3), 81-92. doi.org/10.1080/19312458.2018.1458084
Eine Studie von Tsichla et al. (2021) Gender differences in politicians' Facebook campaigns: Kampagnenpraktiken, Wahlkampfthemen und Wählerengagement kann reproduziert werden.
Der Stand der Forschung zu Geschlechter- und/oder Parteistereotypen im politischen Wahlkampf (Kommunikation von Themen und Eigenschaften) kann am Beispiel eines deutsch- oder englischsprachigen Landes überprüft und aktualisiert werden. Fokus auf die Wahlkampfkommunikation von Politikern zu einer ausgewählten Wahl auf Twitter oder/und anderen Social Media Plattformen. Die Studierenden können eine manuelle oder automatisierte Inhaltsanalyse durchführen.
Tsichla, E., Lappas, G., Triantafillidou, A., & Kleftodimos, A. (2021). Gender differences in politicians’ Facebook campaigns: Campaign practices, campaign issues and voter engagement. New Media & Society. https://doi.org/10.1177/14614448211036405
Magin, M., Podschuweit, N., Haßler, J. & Russmann, U. (2017). Campaigning in the fourth age of political communication. A multi-method study on the use of Facebook by German and Austrian parties in the 2013 national election campaigns. Information, Communication & Society, 20(11), 1698-1719, doi.org10.1080/1369118X.2016.1254269
Dies ist eine Reproduktion einer Studie von Usher et al. (2018) Twitter makes it worse: Political journalists, gendered echo chambers, and the amplification of gender bias. Von Studenten wird erwartet, dass sie/er den Forschungsstand über den Einfluss von Geschlecht und/oder Partei auf die Online-Beziehungen zwischen politischen Journalisten überprüft und aktualisiert. Die/der Studierende kann sich auf ein deutsch- oder englischsprachiges Land konzentrieren. Es kann eine manuelle oder automatisierte Inhaltsanalyse durchgeführt werden.
Alternativ kann die/der Student*in geschlechts- oder parteipolitische Unterschiede in der Kommunikation zwischen (1) politischen Journalisten und Politikern oder (2) Politikern in sozialen Medien untersuchen.
Usher, N., Holcomb, J., & Littman, J. (2018). Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics, 23(3), 324–344. https://doi.org/10.1177/1940161218781254
Research to be replicated: Ye, W., Dorantes-Gilardi, R., Xiang, Z., & Aron, L. (2021). COVID-19 Twitter Communication of Major Societal Stakeholders: Health Institutions, the Government, and the News Media. International Journal Of Communication, 15, 37. Retrieved from https://ijoc.org/index.php/ijoc/article/view/17147
Requirements
For BA students:
1. Construct a comprehensive theoretical framework regarding the research aims.
2. Replicate the data collection procedures inside the example paper
3. Clean the data (remove stopwords, emojis, attached links etc), and make basic descriptive analysis (e.g. the most frequent terms, tweet frequency by account etc).
Research to be replicated: Hagen, L., Keller, T., Neely, S., DePaula, N., & Robert-Cooperman, C. (2018). Crisis Communications in the Age of Social Media: A Network Analysis of Zika-Related Tweets. Social Science Computer Review, 36(5), 523–541. https://doi.org/10.1177/0894439317721985
Requirements
For BA students:
1. Construct a comprehensive theoretical framework regarding the research aims.
2. Replicate the data collection procedures inside the example paper, but a shorter time-frame for the collected dataset is negotiable.
3. Build the (retweet) network with the given resources, calculate and interpret the basic network centrality metrics (degree, betweenness and closeness).
Die Studierenden werden gebeten, eine systematische Replikation von Donzelli et al. (2018) durchzuführen, indem sie diese in einen anderen Kontext stellen (z. B. durch einen anderen Stichprobenansatz, der das Land/die Sprache oder den Zeitraum oder die Menge der zu kodierenden Videos ändert) und/oder durch eine begründete Änderung der ursprünglichen Messinstrumente (z. B. Hinzufügen von Kategorien, Verfeinerung von Kategorien, Änderung der Kategorien insgesamt usw.). Dazu gehören: eine Begründung der Relevanz des Themas; eine Anpassung der Forschungsfragen; eine Anpassung/Transformation der Methoden und des Kodierungsverfahrens; die Durchführung einer deskriptiven Analyse sowie einer einseitigen ANOVA und einer Chi-Quadrat-Analyse. Ziel ist es, die gesamte Studie zu replizieren, indem der Arbeitsablauf der ursprünglichen Autoren befolgt wird.
Donzelli, G., Palomba, G., Federigi, I., Aquino, F., Cioni, L., Verani, M., Carducci, A., & Lopalco, P. (2018). Misinformation on vaccination: A quantitative analysis of YouTube videos. Human Vaccines & Immunotherapeutics, 14(7), 1654-1659. https://doi.org/10.1080/21645515.2018.1454572
Das Hauptziel einer voraussichtlichen Replikation von Shapiro und Park (2015) ist die Replikation der Tabellen 1, 2 und 3. Die Studierenden würden also eine teilweise, aber systematische Replikation durchführen. Außerdem müssen Forschungsfragen und ein klares methodisches Vorgehen ausgearbeitet werden, da sie in der Originalarbeit nicht klar umrissen sind. Generell bestünde die Aufgabe darin, eine aktuelle Begründung für die Wichtigkeit des Themas zu erarbeiten, klare Forschungsfragen abzuleiten, das methodische Vorgehen (die deskriptive Analyse, die manuelle Inhaltsanalyse und die Analyse der häufigsten Begriffe) anzupassen und die oben genannten Ergebnistabellen in einem individuellen Kontext zu replizieren.
Shapiro, M. A., & Park, H. W. (2015). More than entertainment: YouTube and public responses to the science of global warming and climate change. Social Science Information, 54(1), 115–145. https://doi.org/ 10.1177/0539018414554730
Aggressive and malicious user comments are a serious threat in online-communication (Blom, Carpenter, Bowe, & Lange, 2014). Some first studies have shown that behavior in user comments is contagious (Suh, Lee, Suh, Lee, & Lee, 2018) which has implications for the handling of bad behavior online. In this work, you are welcome to analyze social contagion of different quality factors (f.e., rationality or civility) on social media. You have to create a dataset and operationalize discourse quality so you can use computational methods e.g. supervised analyses like sentiment analysis (Wang & Cardie, 2016) or other dictionary-based methods (Malmasi & Zampieri, 2017).
Blom, R., Carpenter, S., Bowe, B. J., & Lange, R. (2014). Frequent contributors within US newspaper comment forums: An examination of their civility and information value. American Behavioral Scientist, 58(10), 1314-1328.
Malmasi, S., & Zampieri, M. (2017). Detecting hate speech in social media. arXiv preprint arXiv:1712.06427.
Suh, K. S., Lee, S., Suh, E. K., Lee, H., & Lee, J. (2018). Online Comment Moderation Policies for Deliberative Discussion–Seed Comments and Identifiability. Journal of the Association for Information Systems, 19(3), 2.
Wang, L., & Cardie, C. (2016). A piece of my mind: A sentiment analysis approach for online dispute detection. arXiv preprint arXiv:1606.05704.
Research on mental health and well-being evolved during the last decade from self-reported data to the increasing use of available observational data. While self-report measures remain popular nowadays, observational approaches enable researchers to use the vast amounts of data available online. In this work, you are welcome to analyze the underlying structures of interaction and communication in online support groups. You have to create a dataset and develop/use a computational research approach to detect impactful intervention methods.
Seale, C., Ziebland, S., & Charteris-Black, J. (2006). Gender, cancer experience and internet use: a comparative keyword analysis of interviews and online cancer support groups. Social science & medicine, 62(10), 2577-2590.
Yang, D., Yao, Z., & Kraut, R. (2017, May). Self-disclosure and channel difference in online health support groups. In Proceedings of the... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media (Vol. 2017, p. 704). NIH Public Access.
Zhang, S., Grave, E., Sklar, E., & Elhadad, N. (2017). Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks. Journal of biomedical informatics, 69, 1-9.
Zhang, S., O’Carroll Bantum, E., Owen, J., Bakken, S., & Elhadad, N. (2017). Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact. Journal of the American Medical Informatics Association, 24(2), 451-459.
Students are invited to propose studies in the mentioned areas or to find them together with the supervisor and to replicate them in the context of the bachelor/master thesis. A replication does not have to (and will not) be a 100% congruent implementation of the chosen study. Rather, a suitable framework for the Bachelor's/Master's thesis will be determined in exchange with the supervisor.
An exemplary procedure for the study Multilevel Emotion Transfer on YouTube: Disentangling the Effects of Emotional Contagion and Homophily on Video Audiences by Rosenbusch and colleagues (2019) could consist of the following steps:
The diffusion of conspiracy theories (Sunstein & Vermeule, 2009) is increasing due to the use of social media. YouTube is one of the most popular platforms worldwide (Alexa, 2018) and is lately under scrutiny to host highly questionable content, among others videos on different conspiracy theories. Here the master candidate should define different conspiracy theories of interest, retrieve data form YouTube and analyze this data (video descriptions, comments) via manual and / or automatic content and/or network analysis. A special emphasis can be put on the content of the comments, or the structure of communication and group dynamics.
Alexa. (2018). The top 500 sites on the web. Retrieved from www.alexa.com/topsites
Oliver, J. E. & Wood, T. J. (2014). Conspiracy Theories and the Paranoid Style(s) of Mass Opinion. American Journal of Political Science, 58(4), 952-966.
Sunstein, C. R. & Vermeule, A. (2009). Conspiracy Theories: Causes and Cures. Journal of Political Philosophy, 17(2), 202-227.
Wood, M. J., Douglas, K. M., & Sutton, R. M. (2012). Dead and Alive. Social Psychological and Personality Science, 3(6), 767–773. doi.org/10.1177/1948550611434786
Interpersonal relationships are exchange-based and able to provide valuable forms of “social currency” (Tardy, 1985) including emotional support (e.g.comfort), instrumental support (e.g. tangible tasks), and informational support. Social media are nowadays used not only to gather information, connect, and make new friends but also to publicly express griefs or frustrations regarding personal or professional events (Lee, 2011). Thus, the exchange of social support is nowadays increasingly organized via social media platforms (e.g. Facebook, Twitter, Youtube or Reddit). While there are numerous studies on social support exchange in Facebook, other platforms are neglected so far. In the master thesis students are welcome to conduct a study on the topic in Reddit or YouTube.
Barrera, M. (1986). Distinctions between social support concepts, measures, and models. American Journal of Community Psychology, 14(4), 413-445. http://dx.doi.org/10.1007/BF00922627
Lee, C. S. (2011). Exploring emotional expressions on YouTube through the lens of media system dependency theory. New media & society, 14, 457-475. doi:0.1177/1461444811419829
Mo, P. K., & Coulson, N. S. (2008). Exploring the Communication of Social Support within Virtual Communities: A Content Analysis of Messages Posted to an Online HIV/AIDS Support Group. Cyberpsychology & Behavior,11(3), 371-374. doi:10.1089/cpb.2007.0118
Tardy, C. H. (1985). Social support measurement. American journal of community psychology, 13(2), 187-202.
Wang, Y.-C., Kraut, R. E., Levine, J. M. (2015). Eliciting and Receiving Online Support: Using Computer-Aided Content Analysis to Examine the Dynamics of Online Social Support. Journal of Medical Internet Research 17(4).
According to Parviainen, Kääriäinen, Tihinen, and Teppola (2017), digitalization refers to “the changes associated with the application of digital technology in all aspects of human society” (p. 64). The incorporation of more digital technologies and changing work environment has created a need for employees to seek information and support.
This increased need for support is met through the use of social media such as enterprise social media in organizations (Leonardi, Huysman, & Steinfield, 2013) or outside the organization on specific social media platforms such as question and answer sites (Anderson et al., 2012; Wellman et al., 1996). As the use of special social media platforms for seeking information support increases, the question of how knowledge and support emerge and are transferred in digital environments arises (Fulk & Yuan, 2013). In this master thesis we focus on how individuals use social media to get or provide work-related information and support.
Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2012, August). Discovering value from community activity on focused question answering sites: a case study of stack overflow. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 850-858). ACM.
Fulk, J., & Yuan, Y. C. (2013). Location, motivation, and social capitalization via Enterprise social networking. Journal of Computer-Mediated Communication, 19(1), 20-37.
Gee, L. K., Jones, J., & Burke, M. (2017). Social Networks and labor markets: How strong ties relate to job finding on Facebook’s social network. Journal of Labor Economics, 35(2), 485–518.
Leonardi, P. M., Huysman, M., & Steinfield, C. (2013). Enterprise social media: Definition, history, and prospects for the study of social technologies in organizations. Journal of Computer-Mediated Communication, 19(1), 1-19.
Parviainen, P., Tihinen, M., Kääriäinen, J., & Teppola, S. (2017). Tackling the digitalization challenge: How to benefit from digitalization in practice. International journal of information systems and project management, 5(1), 63-77.
Utz, S. (2015). Is LinkedIn making you more successful? The informational benefits derived from public social media. New Media & Society, 18(11), 2685-2702.
Wang, Y.-C., Kraut, R. E., Levine, J. M. (2015). Eliciting and Receiving Online Support: Using Computer-Aided Content Analysis to Examine the Dynamics of Online Social Support. Journal of Medical Internet Research 17(4).
Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C. (1996). Computer networks as social networks: Collaborative work, telework, and virtual community. Annual review of sociology, 22(1), 213-238.
Government plays an important role in dealing with unexpected crises and emergencies (e.g. epidemic outbreaks, natural disasters etc) (Wang et al., 2021), one of the most important responsibilities for the governments is to coordinate and deliver timely and consistent messages, so that the cooperation between government agencies/organizations would be more efficient, and by this way, the public could make better preparation to the unanticipated problems (Reynolds and Seeger, 2005). This topic is especially addressed to analyzing the official information during crisis events, the data source is unlimited, students are supposed to collect research data through official platforms/channels (e.g. social media, official websites, press release etc), and later these data will be analyzed by computational methods, an application of automatic content analysis and text mining techniques is highly recommended.
References:
Wang, Y., Hao, H., & Platt, L. S. (2021). Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter. Computers in human behavior, 114, 106568.
Reynolds, B., & Seeger, M. W (2005). Crisis and emergency risk communication as an integrative model. Journal of health communication, 10(1), 43-55.
Opinion leader(s) is not something new in the research of communication science, they have been defined as “the individuals who were likely to influence other persons in their immediate environment” (Katz, 1957; Katz and Lazarsfeld, 1955, p.3). In the era of social media, opinion leaders have shown a great power in different kinds of events (political election, emergency situations, social movements etc.) (e.g. Hagen et al., 2018; Park, 2013; Park and Kaye, 2017), they have greater intentions regarding information seeking, mobilization and public expression than other public users, they also make a significant contribution to individuals’ involvement in (political) processes (Park, 2013). This topic aims to analyze the role of opinion leaders on social media, furthermore, this topic intends to answer the questions such as: how do the opinion leaders affect online communities? how do opinion leaders mobilize the general public? Etc. The students are suggested to collect research data from social media platforms, and the opinion leader(s) would be detected from social network approach, later, depending on the research question(s) and objective(s), a deeper analysis (computational text or visual analysis) is applicable.
References:
Hagen, L., Keller, T., Neely, S., DePaula, N., & Robert-Cooperman, C. (2018). Crisis communications in the age of social media: A network analysis of Zika-related tweets. Social Science Computer Review, 36(5), 523-541.
Katz, E. (1957) 'The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis'. Public Opinion Quarterly 21(1): 61.
Katz, E. and P. F. Lazarsfeld (1955) Personal Influence: The Part Played by People in the Flow of Mass Communications. New York: Free Press.
Park, C. S. (2013) 'Does Twitter Motivate Involvement in Politics? Tweeting, Opinion Leadership, and Political Engagement'. Computers in Human Behavior 29(4): 1641-1648.
Park, C. S. and B. K. Kaye (2017) 'The Tweet Goes On: Interconnection of Twitter Opinion Leadership, Network Size, and Civic Engagement'. Computers in Human Behavior 69: 174-80.
A student can reproduce a study by Tsichla et al. (2021) Gender differences in politicians' Facebook campaigns: Campaign practices, campaign issues and voter engagement (https://doi.org/10.1177/14614448211036405).
State of research on gender and/or party stereotypes in political campaigning (communication of issues and traits) can be reviewed and updated using the example of a German- or English-speaking country. Focus on politicians' campaign communication related to a selected election on Twitter or/and other social media platform(s). The student can perform an automated content analysis.
Tsichla, E., Lappas, G., Triantafillidou, A., & Kleftodimos, A. (2021). Gender differences in politicians’ Facebook campaigns: Campaign practices, campaign issues and voter engagement. New Media & Society. https://doi.org/10.1177/14614448211036405
Magin, M., Podschuweit, N., Haßler, J. & Russmann, U. (2017). Campaigning in the fourth age of political communication. A multi-method study on the use of Facebook by German and Austrian parties in the 2013 national election campaigns. Information, Communication & Society, 20(11), 1698-1719, doi.org10.1080/1369118X.2016.1254269
his is a reproduction of a study by Usher et al. (2018) Twitter makes it worse: Political journalists, gendered echo chambers, and the amplification of gender bias. A student working on this thesis is expected to review and update state of research on the influence of gender and/or party on the online relationships between political journalists. The student can focus on a German- or English-speaking country. An automated content analysis can be applied.
As an alternative option, the student can examine (gender or party) differences in communication between (1) political journalists and politician or (2) politicians on social media.
Usher, N., Holcomb, J., & Littman, J. (2018). Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics, 23(3), 324–344. https://doi.org/10.1177/1940161218781254
The main goal of a prospective replication on Shapiro and Park (2015) is to replicate the tables 1, 2, and 3. Thus, students would conduct a partly but systematic replication. Further, research questions and a clear methodological procedure have to be worked out as they are not clearly outlined in the original paper. In general, the task would be to give a recent justification of the topic's importance, to deduce clear research questions, to adapt the methodological approach (the descriptive analysis, manual content analysis, and analysis of most frequent terms), and to replicate the aforementioned result tables.
Source:
Shapiro, M. A., & Park, H. W. (2015). More than entertainment: YouTube and public responses to the science of global warming and climate change. Social Science Information, 54(1), 115–145. https://doi.org/10.1177/0539018414554730
Students are asked to conduct a systematic replication of Donzelli et al. (2018) by putting it in another context (e.g. through a different sampling approach that changes the country/language or the time period or the amount of videos to code) and/or through a justified change of the original measurement tools (e.g. adding categories, refining categories, changing categories overall, etc.). This includes: a justification of the topic's relevance; an adaptation of the research questions; an adaptation/transformation of the methods and coding procedure; conducting descriptive analysis as well as a one-way ANOVA and a Chi-Square analysis. The goal is to replicate the entire study by following the workflow of the original authors.
Source:
Donzelli, G., Palomba, G., Federigi, I., Aquino, F., Cioni, L., Verani, M., Carducci, A., & Lopalco, P. (2018). Misinformation on vaccination: A quantitative analysis of YouTube videos. Human Vaccines & Immunotherapeutics, 14(7), 1654-1659. https://doi.org/10.1080/21645515.2018.1454572