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Auflistung nach Schlagwort "Twitter"

1 - 10 von 21
  • Konferenzbeitrag
    Analyse wissenschaftlicher Konferenz-Tweets mittels Codebook und der Software Tweet Classifier
    (Workshop Gemeinschaften in Neuen Medien (GeNeMe) 2017, 2017) Lemke, Steffen; Mazarakis, Athanasios
    Mit seiner fokussierten Funktionsweise hat der Mikrobloggingdienst Twitter im Laufe des vergangenen Jahrzehnts eine beachtliche Präsenz als Kommunikationsmedium in diversen Bereichen des Lebens erreicht. Eine besondere Weise, auf die sich die gestiegene Sichtbarkeit Twitters in der täglichen Kommunikation häufig manifestiert, ist die gezielte Verwendung von Hashtags. So nutzen Unternehmen Hashtags um die auf Twitter stattfindenden Diskussionen über ihre Produkte zu bündeln, während Organisatoren von Großveranstaltungen und Fernsehsendungen durch Bekanntgabe ihrer eigenen, offiziellen Hashtags Zuschauer dazu ermutigen, den Dienst parallel zum eigentlichen Event als Diskussionsplattform zu nutzen. [... aus der Einleitung]
  • Zeitschriftenartikel
    Argument Mining on Twitter: A survey
    (it - Information Technology: Vol. 63, No. 1, 2021) Schaefer, Robin; Stede, Manfred
    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.
  • Konferenzbeitrag
    Automatisierte Analyse Radikaler Inhalte im Internet
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Vogel, Inna; Regev, Roey; Steinebach, Martin
    Rassismus, Antisemitismus, Sexismus und andere Diskriminierungs- und Radikalisierungsformen zeigen sich auf unterschiedliche Arten im Internet. Es kann als Satire verpackt sein oder als menschenverachtende Parolen. Sogenannte Hassrede ist für die Kommunikationskultur ein Problem, dem die betroffenen Personen oder Personengruppen ausgesetzt sind. Zwar gibt es den Volksverhetzungsparagraphen (§ 130 StGB), Hassrede liegt allerdings nicht selten außerhalb des justiziablen Bereichs. Dennoch sind hasserfüllte Aussagen problematisch, da sie mit falschen Fakten Gruppierungen radikalisieren und Betroffene in ihrer Würde verletzen. 2017 stellte die Bundesregierung das Netzwerkdurchsetzungsgesetz vor, welches die sozialen Netzwerke dazu zwingt, Hassrede konsequent zu entfernen. Ohne eine automatisierte Erkennung ist dieses aber nur schwer möglich. In unserer Arbeit stellen wir einen Ansatz vor, wie solche Inhalte mithilfe des maschinellen Lernens erkannt werden können. Hierfür werden zunächst die Begriffe Radikalisierung und Hate Speech sprachlich eingeordnet. In diesem Zusammenhang wird darauf eingegangen wie Textdaten bereinigt und strukturiert werden. Anschließend wird der k-Nearest-Neighbor-Algorithmus eingesetzt, um Hate Speech in Tweets zu erkennen und zu klassifizieren. Mit unserem Vorgehen konnten wir einen Genauigkeitswert von 0,82 (Accuracy) erreichen - dieser zeigt die Effektivität des KNN-Klassifikationsansatzes.
  • Konferenzbeitrag
    Customer Service in Social Media: An Empirical Study of the Airline Industry
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Carnein, Matthias; Homann, Leschek; Trautmann, Heike; Vossen, Gottfried; Kraume, Karsten
    Until recently, customer service was exclusively provided over traditional channels. Cus- tomers could write an email or call a service center if they had questions or problems with a product or service. In recent times, this has changed dramatically as companies explore new channels to offer customer service. With the increasing popularity of social media, more companies thrive to provide customer service also over Facebook and Twitter. Companies aim to provide a better customer ex- perience by offering more convenient channels to contact a company. In addition, this unburdens traditional channels which are costly to maintain. This paper empirically evaluates the performance of customer service in social media by analysing a multitude of companies in the airline industry. We have collected several million customer service requests from Twitter and Facebook and auto- matically analyzed how efficient the service strategies of the respective companies are in terms of response rate and time.
  • Textdokument
    Distance Decay Effect and Spatial Interaction during the COVID-19 Pandemic
    (SKILL 2021, 2021) Wolz, Nicolas; Xu, Manning; Wang, Tiantian
    In computational communication science, social network data can be used to analyze trends in the communication behavior of people. For this work, a data set containing english Tweets was provided by the University of Technology Ilmenau, which was collected during the begining of the COVID-19 pandemic. The goal was to find hidden patterns within the data to show if and how the pandemic influenced our communication. This paper looks at the Distance Decay Effect, which says that near things are more related to each other than distant things, and therefore communication should get more sparse the greater the distance between users. Modeling the data with a Gravity Model shows that this relationship is true for the data provided, therefore reproducing earlier research on this topic. We were not successful in finding any clear trend showing that the strengh of the Distance Decay Effect changed over the course of the first weeks of the pandamic.
  • Zeitschriftenartikel
    Exploring the Social Media Impact of Voting Advice Apps: A Case Study on the Representation of the Wahl-O-Mat on Twitter
    (i-com: Vol. 16, No. 3, 2017) Fischer, Moritz Valentin; Tschochohei, Philipp; Anders, Laura; Breuer, Kimberly Dana; Hermida Carrillo, Alejandro; Diefenbach, Sarah
    Voting Advice Applications (VAAs) are web-based tools designed to help voters to find a political party that matches their political views. In the past decade, VAAs have been developed in several countries in order to stimulate political discussion especially among the young and to facilitate a voting decision. At the same time, social media such as Twitter play an increasingly important role for political discussion and opinion formation. The aim of the present research is to explore the interplay of VAAs and social media. We analyzed 500 tweets regarding the main VAA in Germany, the ‘Wahl-O-Mat’, during the pre-election phase of the federal state election in North Rhine Westphalia. As a main result, we discovered that tweets that recommended the app as a product did not obtain high levels of social impact, whereas tweets with self-portrayal content (e.g., posting one’s own VAA result) elicited more engagement by other twitter users. Further results are interpreted through the lens of psychological theories. Finally, we outline practical implications for potential product improvement of the Wahl-O-Mat. Altogether, the present paper highlights the importance of integrating psychological research in the process of VAA development.
  • Konferenzbeitrag
    #Hochwasser – Visuelle Analyse von Social Media im Bevölkerungsschutz / #Hochwasser – Using Visual Analytics of social media in civil protection
    (i-com: Vol. 13, No. 1, 2014) Zisgen, Julia; Kern, Julia; Thom, Dennis; Ertl, Thomas
    Anhand des Hochwassers 2013 in Deutschland soll in diesem Artikel untersucht werden, ob sich die Lageeinschätzung zur Krisenreaktion durch die Auswertung von Social Media-Daten verbessern lässt. Dabei soll insbesondere gezeigt werden, dass Techniken zur computergestützten explorativen Datenanalyse geeignet sind, um trotz der noch recht dünnen Datenlage relevante Erkenntnisse zu gewinnen. Neben einer allgemeinen Erörterung zu Nutzen und Möglichkeiten von Social Media-Daten im Bevölkerungsschutz wird dabei Scatterblogs, ein bestehendes interaktives Social Media- Analysewerkzeug, kurz vorgestellt und evaluiert. Dabei werden Daten verwendet, die während des Hochwassers aufgezeichnet wurden.
  • Zeitschriftenartikel
    Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis
    (Business & Information Systems Engineering: Vol. 66, No. 3, 2024) Baum, Katharina; Baumann, Annika; Batzel, Katharina
    The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes.
  • Konferenzbeitrag
    IT for Peace? Fighting Against Terrorism in Social Media – An Explorative Twitter Study
    (i-com: Vol. 16, No. 2, 2017) Reuter, Christian; Pätsch, Katja; Runft, Elena
    The Internet and especially social media are not only used for supposedly good purposes. For example, the recruitment of new members and the dissemination of ideologies of terrorism also takes place in the media. However, the fight against terrorism also makes use of the same tools. The type of these countermeasures, as well as the methods, are covered in this work. In the first part, the state of the art is summarized. The second part presents an explorative empirical study of the fight against terrorism in social media, especially on Twitter. Different, preferably characteristic forms are structured within the scope with the example of Twitter. The aim of this work is to approach this highly relevant subject with the goal of peace, safety and safety from the perspective of information systems. Moreover, it should serve following researches in this field as basis and starting point.
  • Zeitschriftenartikel
    Learning to Discover Political Activism in the Twitterverse
    (KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Finn, Samantha; Mustafaraj, Eni
    When analysing social media conversations, in search of the public opinion about an unfolding political event that is being discussed in real-time (e.g., presidential debates, major speeches, etc.), it is important to distinguish between two groups of participants: political activists and the general public. To address this problem, we propose a supervised machine-learning approach, which uses inexpensively acquired labeled data from mono-thematic Twitter accounts to learn a binary classifier for the labels “political activist” and “general public”. While the classifier has a 92 % accuracy on individual tweets, when applied to the last 200 tweets from accounts of a set of 1000 Twitter users, it classifies accounts with a 97 % accuracy. Our work demonstrates that machine learning algorithms can play a critical role in improving the quality of social media analytics and understanding, whose importance is increasing as social media adoption becomes widespread.