Themenangebote für Masterarbeiten
Earlier research has mainly focused on analyzing how males and females are portrayed in media. In turn, research on how individuals respond to and communicate with a person depending on his or her gender is scarce. However, bias in response to gender may lead to discrimination and strengthening of common stereotypes. In this work, you will analyze gender bias in responses to Facebook posts. A dataset created by Voigt, Jurgens, Prabhakaran, Jurafsky, and Tsvetkov (2018) can be employed to conduct research on the topic. Computational methods are required to analyze the dataset.
Voigt, R., Jurgens, D., Prabhakaran, V., Jurafsky, D., and Tsvetkov, Y. (2018, May). RtGender: A Corpus of Responses to Gender for Studying Gender Bias. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018).
oogle News has been found to demonstrate bias in their algorithms favoring a small number of significant outlets (Haim, Graefe, & Brosius, 2017; Trielli & Diakopoulos, 2019). According to Haim, Graefe and Brosius (2017), found prevalence of conservative outlets in Google News search results. You are welcome to select any other search engine. For instance, one of the options could be Yandex, the major Russian search engine. It claims to provide their user with unbiased and diverse news content. In order to investigate whether this holds true, you are welcome to reproduce the above-mentioned studies (Haim, Graefe, & Brosius, 2017; Trielli & Diakopoulos, 2019) using algorithm audit, i.e. making queries by, for instance, issuing repeated queries to a platform and further comparing the results or creating false user accounts or programmatically constructing traffic on a website to collect data on the behavior of an algorithm (Sandvig, Hamilton, Karahalios, & Langbort, 2014).
Trielli, D. and Diakopoulos, N. (2019, April). Search as News Curator: The Role of Google in Shaping Attention to News Information. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19) (p. 453). ACM. doi: doi.org/10.1145/3290605.3300683
Haim, M., Graefe, A., & Brosius, H.-B. (2017). Burst of the Filter Bubble? Digital Journalism, 6(3), 330–343. doi:10.1080/21670811.2017.1338145
Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014, May). Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms. Paper presented at the 64th Annual Meeting of the International Communication Association, Seattle, WA, USA.
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.
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.