Emotions, Demographics and Sociability in Twitter Interactions

The social connections people form online affect the quality of information they receive and their online experience. Although a host of socioeconomic and cognitive factors were implicated in the formation of offline social ties, few of them have been empirically validated, particularly in an online setting. In this study, we analyze a large corpus of geo-referenced messages, or tweets, posted by social media users from a major US metropolitan area. We linked these tweets to US Census data through their locations. This allowed us to measure emotions expressed in the tweets posted from an area, the structure of social connections, and also use that area's socioeconomic characteristics in analysis. %We extracted the structure of online social interactions from the people mentioned in tweets from that area. We find that at an aggregate level, places where social media users engage more deeply with less diverse social contacts are those where they express more negative emotions, like sadness and anger. Demographics also has an impact: these places have residents with lower household income and education levels. Conversely, places where people engage less frequently but with diverse contacts have happier, more positive messages posted from them and also have better educated, younger, more affluent residents. Results suggest that cognitive factors and offline characteristics affect the quality of online interactions. Our work highlights the value of linking social media data to traditional data sources, such as US Census, to drive novel analysis of online behavior.Comment: International Conference on the Web and Social Media (ICWSM2016

The 'who' and 'what' of #diabetes on Twitter

Social media are being increasingly used for health promotion, yet the landscape of users, messages and interactions in such fora is poorly understood. Studies of social media and diabetes have focused mostly on patients, or public agencies addressing it, but have not looked broadly at all the participants or the diversity of content they contribute. We study Twitter conversations about diabetes through the systematic analysis of 2.5 million tweets collected over 8 months and the interactions between their authors. We address three questions: (1) what themes arise in these tweets?, (2) who are the most influential users?, (3) which type of users contribute to which themes? We answer these questions using a mixed-methods approach, integrating techniques from anthropology, network science and information retrieval such as thematic coding, temporal network analysis, and community and topic detection. Diabetes-related tweets fall within broad thematic groups: health information, news, social interaction, and commercial. At the same time, humorous messages and references to popular culture appear consistently, more than any other type of tweet. We classify authors according to their temporal 'hub' and 'authority' scores. Whereas the hub landscape is diffuse and fluid over time, top authorities are highly persistent across time and comprise bloggers, advocacy groups and NGOs related to diabetes, as well as for-profit entities without specific diabetes expertise. Top authorities fall into seven interest communities as derived from their Twitter follower network. Our findings have implications for public health professionals and policy makers who seek to use social media as an engagement tool and to inform policy design.Comment: 25 pages, 11 figures, 7 tables. Supplemental spreadsheet available from http://journals.sagepub.com/doi/suppl/10.1177/2055207616688841, Digital Health, Vol 3, 201

Tracking Sentiments toward Fat Acceptance over a Decade on Twitter

The fat acceptance (FA) movement aims to counteract weight stigma and discrimination against individuals who are overweight/obese. We developed a supervised neural network model to classify sentiment toward the FA movement in tweets and identify links between FA sentiment and various Twitter user characteristics. We collected any tweet containing either “fat acceptance” or “#fatacceptance” from 2010–2019 and obtained 48,974 unique tweets. We independently labeled 2000 of them and implemented/trained an Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) neural network that incorporates transfer learning from language modeling to automatically identify each tweet’s stance toward the FA movement. Our model achieved nearly 80% average precision and recall in classifying “supporting” and “opposing” tweets. Applying this model to the complete dataset, we observed that the majority of tweets at the beginning of the last decade supported FA, but sentiment trended downward until 2016, when support was at its lowest. Overall, public sentiment is negative across Twitter. Users who tweet more about FA or use FA-related hashtags are more supportive than general users. Our findings reveal both challenges to and strengths of the modern FA movement, with implications for those who wish to reduce societal weight stigma

Happiness is assortative in online social networks

Social networks tend to disproportionally favor connections between individuals with either similar or dissimilar characteristics. This propensity, referred to as assortative mixing or homophily, is expressed as the correlation between attribute values of nearest neighbour vertices in a graph. Recent results indicate that beyond demographic features such as age, sex and race, even psychological states such as "loneliness" can be assortative in a social network. In spite of the increasing societal importance of online social networks it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that general happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6 month record of their individual tweets, is indeed assortative across the Twitter social network. To our knowledge this is the first result that shows assortative mixing in online networks at the level of SWB. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important instrument in better understanding how both positive and negative sentiments spread through online social ties. Future research may focus on how event-specific mood states can propagate and influence user behavior in "real life".Comment: 17 pages, 9 figure

You Are What You Tweet: Connecting the Geographic Variation in America\u27s Obesity Rate to Twitter Content

We conduct a detailed investigation of the relationship among the obesity rate of urban areas and expressions of happiness, diet and physical activity on social media. We do so by analyzing a massive, geo-tagged data set comprising over 200 million words generated over the course of 2012 and 2013 on the social network service Twitter. Among many results, we show that areas with lower obesity rates: (1) have happier tweets and frequently discuss (2) food, particularly fruits and vegetables, and (3) physical activities of any intensity. Additionally, we provide evidence that each of these results offer different and unique insight into the variation of the obesity rate in urban areas within the United States. Our work shows how the contents of social media may potentially be used to estimate real-time, population-scale measures of factors related to obesity