Unveiling the drivers of active participation in social media discourse

Abstract

The emergence of new public forums in the form of online social media has introduced unprecedented challenges to public discourse, including polarization, misinformation, and the rise of echo chambers. Existing research has extensively examined these topics by focusing on the active actions performed by users, without accounting for the share of individuals who consume content without actively interacting with it. In contrast, this study incorporates passive consumption data to investigate the prevalence of active participation in online discourse. We introduce a metric to quantify the share of active engagement and analyze over 17 million pieces of content linked to a polarized Twitter debate to understand its relationship with several features of online environments, such as echo chambers, coordinated behavior, political bias, and source reliability. Our findings reveal a significant proportion of users who consume content without active interactions, underscoring the importance of considering also passive consumption proxies in the analysis of online debates. Furthermore, we found that increased active participation is primarily correlated with the presence of multimedia content and unreliable news sources, rather than with the ideological stance of the content producer, suggesting that active engagement is independent of echo chambers. Our work highlights the significance of passive consumption proxies for quantifying active engagement, which influences platform feed algorithms and, consequently, the development of online discussions. Moreover, it highlights the factors that may encourage active participation, which can be utilized to design more effective communication campaigns.

Fig. 1

Latent ideology distribution and retweet network of users and influencers. (a) Histogram of users’ and influencers’ ideology score (top) and retweeters’ ideology score distributions for the top 20 influencers (bottom), obtained with the latent ideology estimation algorithm. Negative values represent pro-military aid alignment, while positive values correspond to military aid opponents. Bar colors in the top chart represent the density of influencers (violet) and users (green). The area below retweeter distributions in the bottom chart is shaded in salmon if an influencer is a supporter of Ukrainian aid, and in black if the influencer is against providing weapons to Ukraine. (b) Influencers’ and users’ retweet networks for nodes with a degree greater than 100. Edges and nodes are colored based on nodes’ latent ideology values. The two panels reveal the polarized nature of the discussion and the existence of two echo chambers, each endorsing opposing views on Ukraine aid.

Fig. 2

User-level active consumption for the four active actions. (a) Joint distribution density of the number of followers and the active engagement with respect to retweets (panel I), replies (panel II), likes (panel III), and quotes (panel IV). (b) Boxplots of the active engagement for the same actions as in panel (a), grouped based on users’ ideologies into UA Supporters and Opponents.

Fig. 3

Active engagement level vs content type. (a) Subplots show, for each action, the distribution of active engagement received by tweets having/ not having attached a media. (b) Subplots display, for each action, the distribution of active engagement received by tweets having attached different types of media, i.e., photo, video, GIF.

Fig. 4

Influence of news sources’ political leaning and reliability on active engagement. (a) Boxplot distribution of active engagement with respect to sources’ political leaning for each action. (b) Active engagement vs the number of unique sharers for news sources, colored by reliability.

Fig. 5

Distribution of Active Engagement (AE) for content created by coordinated (red) and non-coordinated (green) accounts across each action. While non-coordinated accounts tend to display a broader AE range, coordinated accounts show a small secondary peak at high AE values for retweets.