Social media behavior during uprisings: selective sharing and avoidance in the China (Hong Kong), Iran, Iraq, and Lebanon protests

Abstract Purpose This study examines the use of social media by individuals during protests in China (Hong Kong), Iraq, Iran, and Lebanon. Method Surveys in the four countries assess the relationship between people’s attitudes toward the protests and their selection bias on social media, manifested through selective sharing and selective avoidance. Findings Regardless of the different political and media systems in each country, social media usage was largely similar. Overall, our findings established that people’s attitude strength toward the protests was associated with their selective sharing behavior; those who scored high on supporting the protests were more likely than those who scored high on opposing the protests to share news that supports the protests, and vice versa. As for selective avoidance, social media protest news use emerged as the strongest predictor. The more individuals followed and shared protest news on social media, the more likely they were to engage in selective avoidance by hiding or deleting comments, unfriending or unfollowing people, and blocking or reporting people for posting comments with which they disagreed. Implications For selective sharing, our findings are consistent with extant research that found individuals with strong attitudes toward certain issues are more likely to express their opinions on social media. Also, for selective avoidance, our study supports the literature, which shows individuals practice selective avoidance to clean up their environment from attitude-inconsistent information, especially on social media, and exceedingly so during protests and crises. Value Selection bias places individuals into secluded groups and contributes to political divisions and polarization. Research has focused on online selective exposure and on offline selective avoidance, but online selective avoidance and sharing have rarely been studied. Our study contributes to emerging research on selective sharing and selective avoidance online during a period of polarization in multiple countries.