Members with intergroup contact are more likely to use negative language in online NBA discussion forums

Jason Shuo Zhang
Sep 11, 2019 · 8 min read

This blog summarizes our CSCW 2019 paper “Intergroup Contact in the Wild: Characterizing Language Differences between Intergroup and Single-group Members in NBA-related Discussion Forums.” by Jason Shuo Zhang, Chenhao Tan, and Qin Lv.

In recent years, there is growing concern about polarization and tribalism in world politics. People can identify themselves as members of a social “tribe” and develop hostile attitudes towards other groups. In the era of social media, platforms, such as Twitter, Reddit, and Facebook, may have led American Republicans and Democrats to further segregate themselves.

In order to reduce prejudice and build bridges between people with different ideologies, a proposed strategy is to take people out of their “echo-chambers” and encourage intergroup contact with opposing groups. This strategy has been proven to be effective in many offline settings as interpersonal interactions can challenge the stereotypes people may have with each other. Some service providers in Silicon Valley are experimenting with this strategy on their social media platforms. In an interview with The Washington Post in 2018, Twitter CEO Jack Dorsey said his company was testing features that would promote alternative viewpoints in Twitter’s timeline to address misinformation and reduce “echo chambers.”

Photo by Tom Brenner for The New York Times

However, the proposed strategy may not achieve ideal outcomes. Actually, it has been tested on the online platform by several experimental studies lately, and we have seen conflicting results: intergroup can both have positive and negative effects. With these contradictory results in prior studies, we believe that observational research allows researchers to characterize intergroup contact in the wild and provide valuable complementary evidence in diverse contexts. Indeed, with the emergence of online groups, it has become possible to observe intergroup contact and individual behavior at a massive scale for substantial periods.

Intergroup setting in NBA-related communities on Reddit

We leverage the existing structure of NBA-related discussion forums of Reddit to identify the group affiliations of users and intergroup contact in the context of professional sports, a novel domain different from politics. We choose online fan groups of professional sports teams as our testbed for the following reasons:

  1. Professional sports play a significant role in modern life. People in the United States spent more than 31 billion hours watching sports games in 2015, and the attendance of the 2017–2018 National Basketball Association (NBA) season reached 22 million.
  2. Similar to the context of politics, professional sports are unambiguously competitive in nature. Fans of sports teams can treat fans of opposing teams as enemies and sometimes even engage in violence. Sports fans also tend to think that the media and supporters from opposing teams are likely to have unfair opinions against their favored teams, just like people with different ideologies.
  3. The /r/NBA subreddit is dedicated to interactions between fans of all NBA teams for any discussion related to the NBA. It represents an open and diverse environment where intergroup contact occurs. More importantly, a unique mechanism of /r/NBA, known as flair, allows us to easily identify fans’ team affiliations.
The illustration of NBA-related discussion forums (also known as subreddits) on Reddit.

The above figure illustrates our framework. There are 30 teams in the NBA, and every team has its own discussion forum (henceforth team subreddit) on Reddit, a place where fans of the corresponding team congregate and discuss news, games, and any other topics that are relevant to the team. The central /r/NBA logo represents /r/NBA, where intergroup contact between fans from different teams happens. Given the different nature of team subreddit and /r/NBA, we first define two settings:

  1. Intergroup setting: /r/NBA.
  2. Intragroup setting: each team’s subreddit (e.g., Lakers’ subreddit and Celtics’ subreddit).

Based on these two settings, we determine whether a fan of a team is exposed to intergroup contact based on his/her (lack of) behavior in /r/NBA, which we refer to as intergroup status. To summarize, we categorize fans of a team into the following two categories:

  1. Intergroup (red icons): Fans of a team who posted in both the affiliated team subreddit and /r/NBA in the season.
  2. Single-group (blue icons): Fans of a team who only posted in the affiliated team subreddit but not /r/NBA in the season.

Before the comparison, we make sure that intergroup and single-group members are a priori balanced on any observable features in the affiliated team subreddit, which indicates similar loyalty to the team. To achieve this, we adopt matching techniques: for each single-group member, we match him/her with the most similar unmatched intergroup member from the same affiliated team, where similarity is measured based on observable activity features (check our paper for details).

Intragroup language differences

We find that intergroup members are more emotionally charged in their affiliated team subreddit than single-group members. This is reflected in their use of negative words, swear words, and hate speech comments. As an example, the below figure compares the use of negative words between intergroup and single-group members across all teams (all). We see the same trend in the two largest (GSW, LAL) and smallest team subreddits (DEN, ORL) ranked by the number of subscribers among teams that have at least 100 single-group members. We further show the scatter plot of all 30 teams in the top right to illustrate that our findings are robust across teams. In addition, we also compare the positive language usage between these two groups of members, but we do not find consistent differences between them (see our paper for more details).

The comparison of negative word usage between intergroup and single-group members in the 2018 season.
The top-10 over-represented words used by intergroup (red) and single-group (blue) members in the 2018 season.

To further interpret the difference between intergroup and single-group members in language usage, we identify a list of distinguishing words that are more likely to be used by intergroup and single-group members. The figure above shows that single-group members are more friendly and calm when commenting in the affiliated team subreddit and use more polite words, such as “agree”, “thanks”, and “help”. Also, “seats” suggests that some single-group members are local fans, as they frequently discuss information about attending live games. In comparison, intergroup members use more swear words and talk more about the referees (likely complaining).

Different levels of intergroup contact

Our observational study reveals that members with higher levels of intergroup contact tend to use more negative language, but there exists varying mechanisms in how intergroup contact level affects negative language use. We group users across all 30 team subreddits based on their fraction of comments in the intergroup setting (/r/NBA) and assign a label 1, 2, 3, 4, or 5 to them. A larger label indicates a higher level of intergroup contact. We also assign a label 0 to single-group members. The below figure shows negative language usage differences between members with different intergroup contact levels. Members of higher intergroup contact levels are generally more negative in language usage: They tend to use more negative words and swear words and generate more hate speech comments in the affiliated team subreddit. However, the trends are not necessarily linear. For instance, intergroup members at level 1 do not show significant differences from single-group members in negative word usage on the left, while intergroup members at level 5 present a significant jump from previous levels in both figures.

Negative and swear word usage of members with different intergroup contact levels in the 2018 season.

Intragroup behavior vs. intergroup behavior of the same user

So far, we have shown that intergroup members use more negative language in their affiliated team subreddits. But why is that? By comparing their behavior in the intergroup setting with the intragroup setting, we find that they use even more negative language in the intergroup setting as they use more negative words and swear words and generate more hate speech comments than in the intragroup setting. Note that this comparison naturally controls for the subjects: we compare the same person in two situations. Our results show that although intergroup members are more emotional than single-group members in the affiliated team subreddit, they are not as “outrageous” as they are in the intergroup setting. In comparison, when going to intergroup setting and confronting fans from other team groups, they tend to have more negative interactions and troll each other.

Intergroup members use more negative words in the intergroup setting than in the intragroup setting in the 2018 season.

Could social media be driving polarization?

Twitter, Reddit, and Facebook have become important platforms for political discussions as well as misinformation. Service providers are designing new features that would actively expose people to opposing views. However, the proposed solution may increase polarization. Unlike decades of offline experiments which mostly indicate intimate contact between members of rival groups across an extended period can produce positive effects, the results in Bail et al. and our work suggest that encountering views from opposing groups online may make them even more wedded to their own views. There are several possible explanations of this contrast by examining the possible mechanisms that intergroup contact affects individual attitudes. First, the comments created on social media are usually brief. These short messages without enough context may not enhance knowledge about opposing groups. Second, the discussion structure may facilitate the spread of negative interaction. Cheng et al. examine the evolution of discussions on and show that existing trolling comments in a discussion thread significantly increase the likelihood of future trolling comments. Third, the anonymous, spontaneous nature of communications on social media may not be conducive to cultivating empathy. In summary, intergroup contact may lead to diverging outcomes depending on the environment and the nature of the contact.

Can we design better online discussion forums for different groups?

The findings in our work indicate that social platforms designers should consider strategies to shape intergroup contact online. As hinted above, it is insufficient to recommend users to follow members of opposing groups or opposing views. Better design strategies need to be experimented for encouraging civil and extended intergroup contact. It would also be useful to take into account how different levels of intergroup contact may moderate individual opinions differently. Content moderation can be a promising area for future studies in the context of intergroup contact. Similarly, a powerful way of spreading online information is through social consensus cues and online endorsement (e.g., upvotes, likes). However, simply promoting content with the highest popularity can sometimes be problematic. Earlier research suggests that tweets with more sentiment-laden words are likely to be favorited or retweeted, and politicians may intentionally use this strategy to maximize impacts on Twitter. This type of behavior can generate negative reactions from opposing groups and push the whole discussion to cycle towards more emotionally-laden and potentially polarizing content. It is thus important to develop comment ranking systems that are cognizant of intergroup contact and prioritize constructive interactions.


Research from the ACM conference on computer-supported cooperative work and social computing

Jason Shuo Zhang

Written by

Ph.D. student @CUBoulder & interested in computational social science, mobile computing, and data science.



Research from the ACM conference on computer-supported cooperative work and social computing

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