Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media最新文献

A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities. To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive, and how temporally dynamic it is. By mapping almost 300 Reddit communities into the landscape induced by this typology, we reveal regularities in how patterns of user engagement vary with the characteristics of a community. Our results suggest that the way new and existing users engage with a community depends strongly and systematically on the nature of the collective identity it fosters, in ways that are highly consequential to community maintainers. For example, communities with distinctive and highly dynamic identities are more likely to retain their users. However, such niche communities also exhibit much larger acculturation gaps between existing users and newcomers, which potentially hinder the integration of the latter. More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that structuring the multi-community landscape can lead to a better understanding of the systematic nature of this diversity.

From the Arab Spring to the Occupy Movement, social media has been instrumental in driving and supporting socio-political movements throughout the world. In this paper, we present one of the first social media investigations of an activist movement around racial discrimination and police violence, known as "Black Lives Matter". Considering Twitter as a sensor for the broader community's perception of the events related to the movement, we study participation over time, the geographical differences in this participation, and its relationship to protests that unfolded on the ground. We find evidence for continued participation across four temporally separated events related to the movement, with notable changes in engagement and language over time. We also find that participants from regions of historically high rates of black victimization due to police violence tend to express greater negativity and make more references to loss of life. Finally, we observe that social media attributes of affect, behavior and language can predict future protest participation on the ground. We discuss the role of social media in enabling collective action around this unique movement and how social media platforms may help understand perceptions on a socially contested and sensitive issue like race.

Exposure to frequent crime incidents has been found to have a negative bearing on the well-being of city residents, even if they are not themselves a direct victim. We pursue the research question of whether naturalistic data shared on Twitter may provide a "lens" to understand changes in psychological attributes of urban communities (1) immediately following crime incidents, as well as (2) due to long-term exposure to crime. We analyze half a million Twitter posts from the City of Atlanta in 2014, where the rate of violent crime is three times of the national average. In a first study, we develop a statistical method to detect changes in social media psychological attributes in the immediate aftermath of a crime event. Second, we develop a regression model that uses historical (yearlong) crime to predict Twitter negative emotion, anxiety, anger, and sadness. We do not find significant changes in social media affect immediately following crime in Atlanta. However we do observe significant ability of historical crime to account for heightened negative emotion and anger in the future. Our findings have implications in gauging the utility of social media to infer longitudinal and population-scale patterns of urban well-being.

Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. Internet systems are particularly attractive for disease outbreaks because they can provide data in near real-time and can be verified by individuals around the globe. However, most existing systems have focused on disease monitoring and do not provide a data repository for policy makers or researchers. In order to fill this gap, we analyzed Wikipedia article content. We demonstrate how a named-entity recognizer can be trained to tag case counts, death counts, and hospitalization counts in the article narrative that achieves an F1 score of 0.753. We also show, using the 2014 West African Ebola virus disease epidemic article as a case study, that there are detailed time series data that are consistently updated that closely align with ground truth data. We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.