To find relevant literature, we worked with several methods. Initially, we constructed key categories that would help organizing the literature found. Those categories are „Twitter Datasets“, „Debate Structure“, „Misinformation“, „Social Network Analysis for Twitter“, „Sentiment Analysis“, „COVID-19 on Twitter“ and „Twitter Discussions“. Relevant terms and synonyms were searched for each category to allow broader retrieval. From thereon, the „Snowball System“ helped gathering related work. The tools used included , „Google Scholar“, „ACM Digital Library“, „Google Dataset Search“, „BASE“ and „Regensburger Katalog“.

Twitter as a popular social media platform is widely used for research. A large part of the respective research focuses on specific case studies, like Presidential Elections (Kušen and Strembeck 2018). Bruns and Stieglitz therefore conducted a comparative study with more than 40 studies and retrieved patterns in communication and tools used (e.g. original tweets, replies, URLs) that are similar for specific contexts and topics. Broad research is also available on the topic of Social Network Analysis (SNA) on Twitter with greatly differing topics, like a Tsunami Early Warning Network (Chatfield and Brajawidagda 2012), European Twitter Networks (Ruiz-Soler 2020) or the Digital Humanities community (Grandjean 2016).

A recent paper by Ahmed et al. contains an SNA of „COVID-19 and the 5G conspiracy“ and could serve as an important model for our own research. In this, the authors use classic metrics and highlighted clusters. One of the main findings was that there was a lack of an authority person combating misinformation, even though only about 35% of the tweets actually contained information supporting the theory. As to general spreading of misinformation relating to COVID-19, Freeman et al. showed that in England, approximately 50% of the population show at least some degree of endorsement for such theories which is connected to less adherence to follow government guidelines and, most interestingly for us, higher likelihood of sharing opinions. However, Singh et al. as well as Pulido et al. suggest in their findings that in general, other crisis relevant themes, as well as science-based information are more present in the social network than false informations are. This apparent contradiction will be a focus for our own research.

Lastly, we looked into possibilites regarding the dataset to work with. A large amount of different datasets exist (Banda et al. 2020, Lamsal 2020, Chen et al. 2020) that focus on different aspects of possible research. The decision will be made according to the final topic clarification.

Ahmed, Wasim, et al. “COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.” Journal of Medical Internet Research, vol. 22, no. 5, 2020, p. e19458.

Banda, Juan M., et al. “A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research – an International Collaboration.” ArXiv:2004.03688 [Cs], Apr. 2020, http://arxiv.org/abs/2004.03688.

Bruns, Axel, and Stefan Stieglitz. “Quantitative Approaches to Comparing Communication Patterns on Twitter.” Journal of Technology in Human Services, vol. 30, no. 3–4, 2012, pp. 160–185.

Chatfield, Akemi, and Uuf Brajawidagda. Twitter Tsunami Early Warning Network: A Social Network Analysis of Twitter Information Flows. 2012.

Chen, Emily, et al. “Covid-19: The First Public Coronavirus Twitter Dataset.” ArXiv Preprint ArXiv:2003.07372, 2020.

Freeman, Daniel, et al. “Coronavirus Conspiracy Beliefs, Mistrust, and Compliance with Government Guidelines in England.” Psychological Medicine, May 2020, pp. 1–13, doi:10.1017/S0033291720001890.

Kušen, Ema, and Mark Strembeck. “Politics, Sentiments, and Misinformation: An Analysis of the Twitter Discussion on the 2016 Austrian Presidential Elections.” Online Social Networks and Media, vol. 5, 2018, pp. 37–50.

Lamsal, Rabindra. “Design and Analysis of a Large-Scale COVID-19 Tweets Dataset.” Applied Intelligence, Nov. 2020, doi:10.1007/s10489-020-02029-z.

Pulido, Cristina M., et al. “COVID-19 Infodemic: More Retweets for Science-Based Information on Coronavirus than for False Information.” International Sociology, vol. 35, no. 4, 2020, pp. 377–92, doi:10.1177/0268580920914755.

Ruiz-Soler, Javier. “European Twitter Networks: Toward a Transnational European Public Sphere?” International Journal of Communication, vol. 14, 2020, p. 27.

Singh, Lisa, et al. “A First Look at COVID-19 Information and Misinformation Sharing on Twitter.” ArXiv Preprint ArXiv:2003.13907, 2020.