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.
@cite_33 found that sentiment expressed in tweets posted around 78 census areas of London correlated highly with community socioeconomic well being, as measured by the Index of Multiple Deprivation (i.e., qualitative study of deprived areas in the UK local councils). In another study @cite_6 they found that happy places tend to interact with other happy places, although other indicators such as demographic data and human mobility were not used in their research @cite_24 .
@cite_24 @cite_33 @cite_6
2099352442 86330731 6274171 2123329834
Policy makers are calling for new socio-economic measures that reflect subjective well-being, to complement traditional measures of material welfare as the Gross Domestic Product (GDP). Self-reporting has been found to be reasonably accurate in measuring one's well-being and conveniently tallies with sentiment expressed on social media (e.g., those satisfied with life use more positive than negative words in their Facebook status updates). Social media content can thus be used to track well-being of individuals. A question left unexplored is whether such content can be used to track well-being of entire physical communities as well. To this end, we consider Twitter users based in a variety of London census communities, and study the relationship between sentiment expressed in tweets and community socio-economic well-being. We find that the two are highly correlated: the higher the normalized sentiment score of a community's tweets, the higher the community's socio-economic well-being. This suggests that monitoring tweets is an effective way of tracking community well-being too. A key facet of urban design, planning, and monitoring is measuring communities' well-being. Historically, researchers have established a link between well-being and visibility of city neighbourhoods and have measured visibility via quantitative studies with willing participants, a process that is invariably manual and cumbersome. However, the influx of the world's population into urban centres now calls for methods that can easily be implemented, scaled, and analysed. We propose that one such method is offered by pervasive technology: we test whether urban mobility--as measured by public transport fare collection sensors--is a viable proxy for the visibility of a city's communities. We validate this hypothesis by examining the correlation between London urban flow of public transport and census-based indices of the well-being of London's census areas. We find that not only are the two correlated, but a number of insights into the flow between areas of varying social standing can be uncovered with readily available transport data. For example, we find that deprived areas tend to preferentially attract people living in other deprived areas, suggesting a segregation effect. The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association. We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.