This meta-analytic study reviews empirical research published from 2007 to 2013 with an aim of providing robust conclusions about the relationship between social media use and citizen engagement. It includes 22 studies that used self-reported measures of social media use and participation, with a total of 116 relationships/effects. The results suggest that social media use generally has a positive relationship with engagement and its three sub-categories, that is, social capital, civic engagement, and political participation. More specifically, we find small-to-medium size positive relationships between expressive, informational, and relational uses of social media and the above indicators of citizen engagement. For identity- and entertainment-oriented uses of social media, our analyses find little evidence supporting their relationship with citizen engagement.

The aim of this study is to assess the relationship between video gaming habits and elementary school students' academic performance. More specifically, we seek to examine the usefulness of a distinction between addiction and high engagement and assess the predictive validity of these concepts in the context of scholastic achievement. Three hundred thirty-three children ages 8 to 12 years from two primary schools in Singapore were selected to participate in this study. A survey utilizing Danforth's Engagement-Addiction (II) scale and questions from DSM-IV was used to collect information from the schoolchildren, while their grades were obtained directly from their teachers. The findings indicate that addiction tendencies are consistently negatively related to scholastic performance, while no such relationship is found for either time spent playing games or for video game engagement. The implications of these findings are discussed.

Research on violent video games suggests that play leads to aggressive behavior. A longitudinal study of an online violent video game with a control group tested for changes in aggressive cognitions and behaviors. The findings did not support the assertion that a violent game will cause substantial increases in real-world aggression. The findings are presented and discussed, along with their implications for research and policy.

This study examines the phenomenon of politically motivated selective avoidance on Facebook in the context of the Hong Kong Umbrella Movement protests in 2014. We conceptualize selective avoidance as individual choices that users make to shield themselves from undesirable dissonant views by removing unwanted information and breaking social ties that transmit such information. Given the political turmoil and high level of polarization during the protests, we argue that selective avoidance was related to the socio-psychological factor of perceived out-group threat. We present an analysis of a survey of 769 students from Hong Kong conducted at the height of the street protests. We find that 15.6% of the respondents removed content and/or unfriended a Facebook friend during the protests. The use of Facebook for protest-related information and expression was associated with higher likelihood of selective avoidance, which in turn predicted actual participation in the street protests. The level of perceived out-group threat strengthened the positive relationship between Facebook use and selective avoidance. We thus argue that group conflict in a time of political turmoil may catalyze selective avoidance, transforming a heterogeneous socio-informational environment into a more insulated gated community. Such acts may promote protest participation but also lead to a more fragmented and polarized citizenry.

This study introduces and evaluates the robustness of different volumetric, sentiment, and social network approaches to predict the elections in three Asian countries – Malaysia, India, and Pakistan from Twitter posts. We find that predictive power of social media performs well for India and Pakistan but is not effective for Malaysia. Overall, we find that it is useful to consider the recency of Twitter posts while using it to predict a real outcome, such as an election result. Sentiment information mined using machine learning models was the most accurate predictor of election outcomes. Social network information is stable despite sudden surges in political discussions, for e.g. around elections-related news events. Methods combining sentiment and volume information, or sentiment and social network information, are effective at predicting smaller vote shares, for e.g. vote shares in the case of independent candidates and regional parties. We conclude with a detailed discussion on the caveats of social media analysis for predicting real-world outcomes and recommendations for future work.