Data Analytics, Data Mining and Machine Learning for Social Media

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    Who shapes crisis communication on Twitter? An analysis of German influencers during the COVID-19 pandemic
    ( 2022-01-04) Shahi, Gautam Kishore ; Clausen, Sünje ; Stieglitz, Stefan
    Twitter is becoming an increasingly important platform for disseminating information during crisis situations, such as the COVID-19 pandemic. Effective crisis communication on Twitter can shape the public perception of the crisis, influence adherence to preventative measures, and thus affect public health. Influential accounts are particularly important as they reach large audiences quickly. This study identifies influential German-language accounts from almost 3 million German tweets collected between January and May 2020 by constructing a retweet network and calculating PageRank centrality values. We capture the volatility of crisis communication by structuring the analysis into seven stages based on key events during the pandemic and profile influential accounts into roles. Our analysis shows that news and journalist accounts were influential throughout all phases, while government accounts were particularly important shortly before and after the lockdown was instantiated. We discuss implications for crisis communication during health crises and for analyzing long-term crisis data.
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    Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs
    ( 2022-01-04) Koss, Jonathan ; Bohnet-Joschko, Sabine
    Pharmaceutical companies increasingly must consider patients’ needs in drug development. Since patients’ needs are often difficult to measure, especially in rare diseases, information in drug development decision-making is limited. In the proposed study, we employ the opportunity algorithm to identify and prioritize unmet medical needs of multiple sclerosis patients shared in social media posts. Using topic modeling and sentiment analysis features of the opportunity algorithm are generated. The result implies that sensory problems, pain, mental health problems, fatigue and sleep disturbances represent the highest unmet medical needs of the samples population. The present study suggests a promising potential of this method to provide relevant insights into rare disease populations to promote patient-centered drug development.
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    Perception Analysis: Pro- and Anti- Vaccine Classification with NLP and Machine Learning
    ( 2022-01-04) Okpala, Izunna ; Romera Rodriguez, Guillermo ; Zheng, Weibing ; Halse, Shane ; Kropczynski, Jess
    Online discussion of the ensuing pandemic exemplifies the extent and complexity of information required to understand human perception. Social media has proven to be a viable medium for identifying actionable data and analyzing public perception. As health sectors all over the world battled to obtain accurate information regarding COVID-19, this research focused on gauging public perceptions of the vaccine. The public reception of the vaccine can be determined by public perception. This study explores how to use machine learning to understand human perceptions in the context of the COVID-19 vaccine. Natural Language Processing (NLP) was employed to detect pro- and anti-vaccine tweets, while two machine learning classification models were used to study the patterns derived from the analysis. The study analyzed people's perceptions of the vaccine by presenting the results from a geographic region, while learning patterns that are likely to be associated with pro- or anti-vaccine perceptions.
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    Multi-National Topics Maps for Parliamentary Debate Analysis
    ( 2022-01-04) Schaal, Markus ; Davis, Enno ; Mueller, Roland M.
    In recent years, automated political text processing became an indispensable requirement for providing automatic access to political debate. During the Covid-19 worldwide pandemic, this need became visible not only in social sciences but also in public opinion. We provide a path to operationalize this need in a multi-lingual topic-oriented manner. Using a publicly available data set consisting of parliamentary speeches, we create a novel process pipeline to identify a good reference model and to link national topics to the cross-national topics. We use design science research to create this process pipeline as an artifact.
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    False Rumor (Fake) and Truth News Spread During A Social Crisis
    ( 2022-01-04) Koohikamali, Mehrdad ; Gerhart, Natalie
    During a social crisis, the truthfulness of information becomes very important, particularly in determining if the information will spark extreme social engagement. We test a research model to examine major determinants of message spread during the 2016 Charlotte, North Carolina protests which occurred after false online rumors spread related to the shooting of Keith Lamont Scott. We hypothesize relationships between message spread (retweets) and extremity, negative emotions (sadness and fear), and social ties (reciprocal reply and location proximity), and Twitter experience. Using Poisson regression, we evaluate and compare two separate models (rumor and truth). Results of the analysis indicate that rumors and truths spread differently. More extreme messages spread less if they are truths, and fear does not relate to the spread of rumors. The results of the study provide theoretical and practical insights into the current research in the areas of information diffusion and social engagement.