Twitter as health information source : exploring the parameters affecting dementia-related tweets

Unlike other media, research on the credibility of information present on social media is limited. This limitation is even more pronounced in the case of healthcare, including dementia-related information. The purpose of this study was to identify user groups that show high bot-like behavior and profile features that deviation from typical human behavior. We collected 16,691 tweets about dementia posted over the course of a month by 8400 users. We applied inductive coding to categorize users. The BotOrNot? API was used to compute a bot score. This work provides insight into relations between user features and a bot score. We performed analysis techniques such as Kruskal-Wallis, stepwise multiple variable regression, user tweet frequency analysis and content analysis on the data. These were further evaluated for the most frequently referenced URLs in the tweets and most active users in terms of tweet frequency. Initial results indicated that the majority of users are regular users and not bots. Regression analysis revealed a clear relationship between different features. Independent variables in the user profiles such as geo_data and favourites_count, correlated with the final bot score. Similarly, content analysis of the tweets showed that the word features of bot profiles have an overall smaller percentage of words compared to regular profiles. Although this analysis is promising, it needs further enhancements

Public Perception of Human Applications of CRISPR Gene Editing

This projects goal was to examine American public opinion and media representation of CRISPR gene editing technology and its human applications. To do so, we analyzed three data sources: public opinion polls, online news articles, and tweets. Our findings show that public perception of CRISPR has been changing over time and the frequency of differently toned content (positive, negative, and neutral) fluctuates. Variation across the three types of sources reveals that American public opinion on CRISPR is not consistent

Text Analysis of Air Force References in Twitter

Social media has grown to become a rich source for opinions, authored by individuals who volunteer them, unedited and in real-time. Armed with this information, an organization like the Air Force can understand the perceptions of consumers and learn to better serve the American taxpayer. To accomplish this goal, this research takes a qualitative approach, utilizing social media analytics in combination with various Text Mining methodologies (word frequency, word relationships, sentiment analysis, topic modeling) to provide insight on Air Force related content shared on Twitter. To provide a well-rounded analysis of the overall perception of the Air Force enterprise, the methods mentioned are conducted on Tweets related to the Air Force’s five core missions: Space/Cyberspace, Nuclear Deterrence, Air Superiority, Advancements in Technology, and Intelligence, Surveillance, Reconnaissance. This research also identifies the key players that publish the most engaged Tweets related to the Air Force. By understanding the types of users who possess the most influence (Regular Users, Bloggers, Celebrities, Military Leaders, Politicians, Professional Organizations), Air Force leaders are better equipped to react to content and protect the Air Force brand

A Comparative Analysis of Anti-vax Discourse on Twitter Before and After COVID-19 Onset

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, “government and politics” and “conspiracy theories,” and decreased for “developmental disabilities.” Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue

Data science methods for the analysis of controversial social dedia discussions

Social media communities like Reddit and Twitter allow users to express their views on topics of their interest, and to engage with other users who may share or oppose these views. This can lead to productive discussions towards a consensus, or to contended debates, where disagreements frequently arise. Prior work on such settings has primarily focused on identifying notable instances of antisocial behavior such as hate-speech and “trolling”, which represent possible threats to the health of a community. These, however, are exceptionally severe phenomena, and do not encompass controversies stemming from user debates, differences of opinions, and off-topic content, all of which can naturally come up in a discussion without going so far as to compromise its development. This dissertation proposes a framework for the systematic analysis of social media discussions that take place in the presence of controversial themes, disagreements, and mixed opinions from participating users. For this, we develop a feature-based model to describe key elements of a discussion, such as its salient topics, the level of activity from users, the sentiments it expresses, and the user feedback it receives. Initially, we build our feature model to characterize adversarial discussions surrounding political campaigns on Twitter, with a focus on the factual and sentimental nature of their topics and the role played by different users involved. We then extend our approach to Reddit discussions, leveraging community feedback signals to define a new notion of controversy and to highlight conversational archetypes that arise from frequent and interesting interaction patterns. We use our feature model to build logistic regression classifiers that can predict future instances of controversy in Reddit communities centered on politics, world news, sports, and personal relationships. Finally, our model also provides the basis for a comparison of different communities in the health domain, where topics and activity vary considerably despite their shared overall focus. In each of these cases, our framework provides insight into how user behavior can shape a community’s individual definition of controversy and its overall identity.Social-Media Communities wie Reddit und Twitter ermöglichen es Nutzern, ihre Ansichten zu eigenen Themen zu äußern und mit anderen Nutzern in Kontakt zu treten, die diese Ansichten teilen oder ablehnen. Dies kann zu produktiven Diskussionen mit einer Konsensbildung führen oder zu strittigen Auseinandersetzungen über auftretende Meinungsverschiedenheiten. Frühere Arbeiten zu diesem Komplex konzentrierten sich in erster Linie darauf, besondere Fälle von asozialem Verhalten wie Hassrede und "Trolling" zu identifizieren, da diese eine Gefahr für die Gesprächskultur und den Wert einer Community darstellen. Die sind jedoch außergewöhnlich schwerwiegende Phänomene, die keinesfalls bei jeder Kontroverse auftreten die sich aus einfachen Diskussionen, Meinungsverschiedenheiten und themenfremden Inhalten ergeben. All diese Reibungspunkte können auch ganz natürlich in einer Diskussion auftauchen, ohne dass diese gleich den ganzen Gesprächsverlauf gefährden. Diese Dissertation stellt ein Framework für die systematische Analyse von Social-Media Diskussionen vor, die vornehmlich von kontroversen Themen, strittigen Standpunkten und Meinungsverschiedenheiten der teilnehmenden Nutzer geprägt sind. Dazu entwickeln wir ein Feature-Modell, um Schlüsselelemente einer Diskussion zu beschreiben. Dazu zählen der Aktivitätsgrad der Benutzer, die Wichtigkeit der einzelnen Aspekte, die Stimmung, die sie ausdrückt, und das Benutzerfeedback. Zunächst bauen wir unser Feature-Modell so auf, um bei Diskussionen gegensätzlicher politischer Kampagnen auf Twitter die oben genannten Schlüsselelemente zu bestimmen. Der Schwerpunkt liegt dabei auf den sachlichen und emotionalen Aspekten der Themen im Bezug auf die Rollen verschiedener Nutzer. Anschließend erweitern wir unseren Ansatz auf Reddit-Diskussionen und nutzen das Community-Feedback, um einen neuen Begriff der Kontroverse zu definieren und Konversationsarchetypen hervorzuheben, die sich aus Interaktionsmustern ergeben. Wir nutzen unser Feature-Modell, um ein Logistischer Regression Verfahren zu entwickeln, das zukünftige Kontroversen in Reddit-Communities in den Themenbereichen Politik, Weltnachrichten, Sport und persönliche Beziehungen vorhersagen kann. Schlussendlich bietet unser Modell auch die Grundlage für eine Vergleichbarkeit verschiedener Communities im Gesundheitsbereich, auch wenn dort die Themen und die Nutzeraktivität, trotz des gemeinsamen Gesamtfokus, erheblich variieren. In jedem der genannten Themenbereiche gibt unser Framework Erkenntnisgewinne, wie das Verhalten der Nutzer die spezifisch Definition von Kontroversen der Community prägt