TY - JOUR A1 - Schäfer, Robin A1 - Stede, Manfred T1 - Argument mining on twitter BT - a survey JF - Information technology : it ; Methoden und innovative Anwendungen der Informatik und Informationstechnik ; Organ der Fachbereiche 3 und 4 der GI e.V. und des Fachbereichs 6 der ITG N2 - In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook. KW - Argument Mining KW - Twitter KW - Stance Detection Y1 - 2021 U6 - https://doi.org/10.1515/itit-2020-0053 SN - 1611-2776 SN - 2196-7032 VL - 63 IS - 1 SP - 45 EP - 58 PB - De Gruyter CY - Berlin ER - TY - JOUR A1 - Abramova, Olga A1 - Batzel, Katharina A1 - Modesti, Daniela T1 - Collective response to the health crisis among German Twitter users BT - a structural topic modeling approach JF - International Journal of Information Management Data Insights N2 - We used structural topic modeling to analyze over 800,000 German tweets about COVID-19 to answer the questions: What patterns emerge in tweets as a response to a health crisis? And how do topics discussed change over time? The study leans on the goals associated with the health information seeking (GAINS) model, discerning whether a post aims at tackling and eliminating the problem (i.e., problem-focused) or managing the emotions (i.e., emotion-focused); whether it strives to maximize positive outcomes (promotion focus) or to minimize negative outcomes (prevention focus). The findings indicate four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Public communication is volatile over time, and a shift is evidenced from self-centered to community-centered topics within 4.5 weeks. Our study illustrates social media text mining's potential to quickly and efficiently extract public opinions and reactions. Monitoring fears and trending topics enable policymakers to rapidly respond to deviant behavior, like resistive attitudes toward containment measures or deteriorating physical health. Healthcare workers can use the insights to provide mental health services for battling anxiety or extensive loneliness from staying home. KW - social media KW - Twitter KW - modeling KW - regulatory focus theory KW - crisis management KW - text mining Y1 - 2022 U6 - https://doi.org/10.1016/j.jjimei.2022.100126 SN - 2667-0968 VL - 2 IS - 2 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kapidzic, Sanja A1 - Frey, Felix A1 - Neuberger, Christoph A1 - Stieglitz, Stefan A1 - Mirbabaie, Milad T1 - Crisis communication on Twitter BT - differences between user types in top tweets about the 2015 “refugee crisis” in Germany JF - International journal of communication N2 - The study explores differences between three user types in the top tweets about the 2015 “refugee crisis” in Germany and presents the results of a quantitative content analysis. All tweets with the keyword “Flüchtlinge” posted for a monthlong period following September 13, 2015, the day Germany decided to implement border controls, were collected (N = 763,752). The top 2,495 tweets according to number of retweets were selected for analysis. Differences between news media, public and private actor tweets in topics, tweet characteristics such as tone and opinion expression, links, and specific sentiments toward refugees were analyzed. We found strong differences between the tweets. Public actor tweets were the main source of positive sentiment toward refugees and the main information source on refugee support. News media tweets mostly reflected traditional journalistic norms of impartiality and objectivity, whereas private actor tweets were more diverse in sentiments toward refugees. KW - refugee crisis 2015 KW - Germany KW - social media KW - Twitter KW - user types Y1 - 2023 UR - https://ijoc.org/index.php/ijoc/article/view/18172/4022 SN - 1932-8036 VL - 17 SP - 735 EP - 754 PB - The Annenberg Center for Communication CY - Los Angeles, Calif. ER - TY - JOUR A1 - Hagemann, Linus A1 - Abramova, Olga T1 - Emotions and information diffusion on social media BT - a replication in the context of political communication on Twitter JF - AIS transactions on replication research N2 - This paper presents a methodological and conceptual replication of Stieglitz and Dang-Xuan’s (2013) investigation of the role of sentiment in information-sharing behavior on social media. Whereas Stieglitz and Dang-Xuan (2013) focused on Twitter communication prior to the state parliament elections in the German states Baden-Wurttemberg, Rheinland-Pfalz, and Berlin in 2011, we test their theoretical propositions in the context of the state parliament elections in Saxony-Anhalt (Germany) 2021. We confirm the positive link between sentiment in a political Twitter message and its number of retweets in a methodological replication. In a conceptual replication, where sentiment was assessed with the alternative dictionary-based tool LIWC, the sentiment was negatively associated with the retweet volume. In line with the original study, the strength of association between sentiment and retweet time lag insignificantly differs between tweets with negative sentiment and tweets with positive sentiment. We also found that the number of an author’s followers was an essential determinant of sharing behavior. However, two hypotheses supported in the original study did not hold for our sample. Precisely, the total amount of sentiments was insignificantly linked to the time lag to the first retweet. Finally, in our data, we do not observe that the association between the overall sentiment and retweet quantity is stronger for tweets with negative sentiment than for those with positive sentiment. KW - Twitter KW - information diffusion KW - sentiment KW - elections Y1 - 2023 U6 - https://doi.org/10.17705/1atrr.00079 SN - 2473-3458 VL - 9 IS - 1 SP - 1 EP - 19 PB - AIS CY - Atlanta ER - TY - JOUR A1 - Brendel, Nina A1 - Matzner, Nils A1 - Menzel, Max-Peter T1 - Geographisches Gezwitscher – Analyse von Twitter-Daten als Methode im GW-Unterricht JF - GW-Unterricht N2 - Soziale Medien sind ein wesentlicher Bestandteil des Alltags von Schüler*innen und gleichzeitig zunehmend wichtig in Wirtschaft, Politik und Wissenschaft. Am Beispiel von Twitter zeigt dieser Beitrag, dass soziale Medien im Unterricht auch für die Beantwortung geographischer Fragestellungen verwendet werden können. Hierfür eignen sich Twitter-Daten aufgrund ihrer Georeferenzierung und weiterer interessanter Inhalte besonders. Der Beitrag gibt einen Überblick über die Verwendung von Twitter für sozialwissenschaftliche und humangeographische Fragestellungen und reflektiert die Nutzung von Twitter im Unterricht. Für die Unterrichtspraxis werden Beispiele zu den Themen Braunkohle, Flutereignisse und Raumwahrnehmungen sowie Anleitungen zur Auswertung, Anwendung und Reflexion von Twitter-Analysen vorgestellt. KW - Twitter KW - Soziale Medien KW - Forschungsmethodik KW - Unterrichtsmethoden Y1 - 2021 U6 - https://doi.org/10.1553/gw-unterricht164s72 SN - 2414-4169 SP - 72 EP - 85 PB - Verlag der Österreichischen Akademie der Wissenschaften CY - Wien ER -