Deliberate Exposure to Opposing Views and its Association with Behavior and Rewards on Political Communities

TL;DR

The paper investigates deliberate exposure to opposing political views on Reddit by identifying CMV participants across four political communities and examining reward outcomes, linguistic patterns, and persistence under sanctions. It employs robust matching, validation against partisan signals, content analysis via readability, Perspective API hostility measures, LIWC-22 traits, and LDA topic modeling, plus higher-order Markov chains to model user trajectories. Key findings show CMV participants receive $2.53 ext{\%}$ to $19.22 ext{\%}$ fewer upvotes in their home communities and exhibit more advanced, authentic, and less hostile language, with persistence against downvotes in the short term but possible long-term effects. These results imply that while open-minded, cross-ideology voices are accepted, they are not immediately influential, highlighting the need to promote normative open discourse and long-horizon strategies to amplify such voices. The work advances understanding of online deliberation and informs interventions aimed at reducing polarization by integrating open-minded participation into community norms.

Abstract

Engaging with diverse political views is important for reaching better collective decisions, however, users online tend to remain confined within ideologically homogeneous spaces. In this work, we study users who are members of these spaces but who also show a willingness to engage with diverse views, as they have the potential to introduce more informational diversity into their communities. Across four Reddit communities (r/Conservative, r/The_Donald, r/ChapoTrapHouse, r/SandersForPresident), we find that these users tend to use less hostile and more advanced and personable language, but receive fewer social rewards from their peers compared to others. We also find that social sanctions on the discussion community r/changemyview are insufficient to drive them out in the short term, though they may play a role over the longer term.

Figures (6)

• Figure 1: ECDF of number of CMV comments per participant.
• Figure 2: First-order transition matrix with 8-hour interruptions. +, -, and $\bullet$ show upvoted, downvoted, and neutral comments, respectively.
• Figure 3: 1000-comment Markov chain simulations with all configurations. Community fluctuations are higher with no interruption as each session's starting community is picked randomly. Unsurprisingly, users spend most of their time at home.
• Figure 4: CDF of average comment similarity across users.
• Figure 5: Prominence comparison of all entity types by subreddit group. #case and #control indicate total number of entities detected across all comments. Fac, Language, Quantity and Loc omitted due to $<$ 1% prominence in all groups.