Estimating county health statistics with twitter

Understanding the relationships among environment, behav-ior, and health is a core concern of public health researchers. While a number of recent studies have investigated the use of social media to track infectious diseases such as influenza, lit-tle work has been done to determine if other health concerns can be inferred. In this paper, we present a large-scale study of 27 health-related statistics, including obesity, health insur-ance coverage, access to healthy foods, and teen birth rates. We perform a linguistic analysis of the Twitter activity in the top 100 most populous counties in the U.S., and find a signifi-cant correlation with 6 of the 27 health statistics. When com-pared to traditional models based on demographic variables alone, we find that augmenting models with Twitter-derived information improves predictive accuracy for 20 of 27 statis-tics, suggesting that this new methodology can complement existing approaches

INVESTIGATING CRIME-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH

Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with crime types. Apparently, crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of crimes

Social Bots for Online Public Health Interventions

According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign

A Critical Audit of Accuracy and Demographic Biases within Toxicity Detection Tools

The rise of toxicity and hate speech on social media has become a cause for concern due to their effects on politics and the growth of extremist internet communities. The tools currently used to identify and eliminate harmful content have received widespread criticism from both the public and the academic community for their inaccuracies and biases. In our research, we set out to audit the performance of Perspective API, a toxicity detector created by research teams at Google and Jigsaw, on the language of users across a variety of demographic categories. We draw from Crenshaw\u27s framework of intersectionality to discuss the unique harms that result from existing at the intersections of marginalization and examine existing computational models of disparate impact and proxy discrimination. In addition, we conduct A/B testing on Amazon\u27s Mechanical Turk, a popular crowd-sourcing platform for data annotation within research communities, to identify and discuss biases that arise from human demographic prediction

What are Your Pronouns? Examining Gender Pronoun Usage on Twitter

Stating your gender pronouns, along with your name, is becoming the new norm of self-introductions at school, at the workplace, and online. The increasing prevalence and awareness of nonconforming gender identities put discussions of developing gender-inclusive language at the forefront. This work presents the first empirical research on gender pronoun usage on large-scale social media. Leveraging a Twitter dataset of over 2 billion tweets collected continuously over two years, we find that the public declaration of gender pronouns is on the rise, with most people declaring as using she series pronouns, followed by he series pronouns, and a smaller but considerable amount of non-binary pronouns. From analyzing Twitter posts and sharing activities, we can discern users who use gender pronouns from those who do not and also distinguish users of various gender identities. We further illustrate the relationship between explicit forms of social network exposure to gender pronouns and their eventual gender pronoun adoption. This work carries crucial implications for gender-identity studies and initiates new research directions in gender-related fairness and inclusion, as well as support against online harassment and discrimination on social media.Comment: 23 pages, 11 figures, 2 table

Resurgent Insurgents:Quantitative Research Into Jihadists Who Get Suspended but Return on Twitter

Jihadists are very active on Twitter but their accounts frequently get suspended. A substantial debate over the effectiveness of suspension has arisen; an important factor is that Jihadists quickly create new accounts, resurging back like a game of whack-a-mole. This causes biases for terrorism and intelligence analysts. Whilst widely acknowledged, little research addresses the problem. In this study we identify resurging Jihadist accounts with novel methods, and provide detailed analysis going beyond previous case-studies. We show that suspension is less disruptive to terrorists than previously thought, whilst the bias and disruption caused to terrorism research has been underestimated

Analyzing the Engagement of Social Relationships During Life Event Shocks in Social Media

Individuals experiencing unexpected distressing events, shocks, often rely on their social network for support. While prior work has shown how social networks respond to shocks, these studies usually treat all ties equally, despite differences in the support provided by different social relationships. Here, we conduct a computational analysis on Twitter that examines how responses to online shocks differ by the relationship type of a user dyad. We introduce a new dataset of over 13K instances of individuals' self-reporting shock events on Twitter and construct networks of relationship-labeled dyadic interactions around these events. By examining behaviors across 110K replies to shocked users in a pseudo-causal analysis, we demonstrate relationship-specific patterns in response levels and topic shifts. We also show that while well-established social dimensions of closeness such as tie strength and structural embeddedness contribute to shock responsiveness, the degree of impact is highly dependent on relationship and shock types. Our findings indicate that social relationships contain highly distinctive characteristics in network interactions and that relationship-specific behaviors in online shock responses are unique from those of offline settings.Comment: Accepted to ICWSM 2023. 12 pages, 5 figures, 5 table

Media’s influence on the 21st century society: A global criminological systematic review

This investigation assumes that the media can reduce or spread criminal activities and tendencies based on how the concerned parties apply the policies and community standards that guide these platforms’ use. In total, 254 materials were gathered across several search systems between October 2021 and September 2022. Qualitative data were used from the selected materials to synthesise and summarise the content on the examined 21st-century events and media’s influence on crime. It is not possible to reject the premise that the media influences opinions on crime and the legal system. Nevertheless, the data reveals that no causal media effect can be directly established. However, the same data uncovers how media portrays an activity affects how people perceive it. Advances in technology, media, and criminology may have affected the analysis of records, including the time and quality of resources. More accurate and fair media coverage of crime would lead to a more informed and aware population. On the other hand, media houses that promote and reward good behaviour should be applauded. These two steps ensure the media cannot be ignored when assessing crime and how the public perceives it, as it can encourage crime and shift perceptions. Therefore, further research, stricter laws and policies, and community education on crime prevention and media screening are needed. The fact that unfavourable media coverage of crime can ruin a business, either directly or indirectly (consumer behaviour changes due to crime), makes this paper of utmost importance for businessmen, politicians, and local agencies.Esta dissertação presume que os media podem ser utilizados para reduzir ou difundir atividades ou tendências criminosas, dependendo da aplicação de políticas e padrões comunitários que influenciam tais plataformas. Foram utilizados 254 materiais reunidos em diversos sistemas de pesquisa entre outubro de 2021 e setembro de 2022. Estes compreendem publicações do século XXI que examinam a influência dos media nas práticas criminais e suas perceções. Apesar deste estudo não possibilitar estabelecer uma relação causal, não é, ainda assim, possível rejeitar a premissa de que os media influenciam as perceções face ao crime. Determina, contudo, que o modo como os media divulgam uma atividade afeta a perceção social face à mesma. Uma população mais informada e consciente depende de uma cobertura mediática mais fatual. Os media que promovem e recompensam o bom comportamento devem ser louvados. Os media não podem ser ignorados na avaliação do crime e da sua perceção, tendo o poder de incentivar a criminalidade e potenciar alterações nas perceções sociais. Consequentemente, é necessário investigar mais, aplicar leis e políticas mais rigorosas, e investir em programas de educação comunitária de prevenção à criminalidade e interpretação dos media. Esta dissertação é de elevada importância a empresários, políticos e outros órgãos locais, pelo fato de a cobertura desfavorável do crime pelos media poder arruinar um indivíduo, organização ou até um negócio, seja de forma direta (críticas ao estabelecimento) ou indireta (mudanças no comportamento do consumidor devido à ocorrência de crimes numa região)