1% of all communities initiate 74% of all conflicts on Reddit. The red nodes (communities) in this map initiate a large amount of conflict, and we can see that these conflict intiating nodes are rare and clustered together in certain social regions.
"Come look at all the brainwashed idiots in r/Documentaries
Seriously, none of those people are willing to even CONSIDER
that our own country orchestrated the 9/11 attacks.
They are all 100% certain the “turrists” were behind it all, and
all of the smart people who argue it are getting downvoted
to the depths of hell. Damn shame. Wish people would do
their research. Here's the link."
The above post in reddit.com/r/conspiracy (now deleted) led to several members of r/conspiracy posting uncivil comments (starting a 'raid') on the linked post in reddit.com/r/Documentaries.
Therefore, in this research paper (accepted and to be presented at The Web Conference, WWW 2018), we conduct a data driven analysis of how conflicts/raids occur between communities in Reddit, their impact, mitigation, and prediction.
User-defined communities are an essential component of many web platforms, where users express their ideas, opinions, and share information.
However, despite their positive benefits, online communities also have the potential to be breeding grounds for conflict and anti-social behavior.
Here we used 40 months of Reddit comments and posts (from January 2014 to April 2017) to examine cases of intercommunity conflict ('wars' or 'raids'), where members of one Reddit community, called "subreddit", collectively mobilize to participate in or attack another community.
We discovered these conflict events by searching for cases where one community posted a hyperlink to another community, focusing on cases where these hyperlinks were associated with negative sentiment (e.g., "come look at all the idiots in community X") and led to increased antisocial activity in the target community. We analyzed a total of 137,113 cross-links between 36,000 communities.
Our analysis revealed a number of important trends related to conflict on Reddit, with general implications for intercommunity conflict on the web.
For example, we found that
- A small number of communities initiate most conflicts, with 1% of communities initiating 74% of all conflicts. The image above shows a 2-dimensional map of the various Reddit communities. The red nodes/communities in this map initiate a large amount of conflict.
- Topically similar, but with opposing ideology, groups fight: We can see that these conflict intiating nodes are rare and clustered together in certain social regions. These communities attack other communities that are similar in topic but different in point of view.
- Conflicts are initiated by active community members but are carried out by less active users. It is usually highly active users that post hyperlinks to target communities, but it is more peripheral users who actually follow these links and particpate in conflicts.
- Conflicts are marked by the formation of "echo-chambers", where users in the discussion thread primarily interact with other members of their own community (i.e., "attackers" interact with "attackers" and "defenders" with "defenders").
- Conflicts have long-term adverse effects on the engagement of members of the target community, but these adverse effects are mitigated when the "defending" community members engage in heated, direct debates with the "attackers".
- Conflicts can be defended against when the attacked community directly engages with ('fights back') the attacking users.
Predicting conflicts: We developed a novel deep learning (LSTM-based) model to predict whether a link from one community to another is going to lead to conflict.
In our model, we learn vector representations, or embeddings, of users and communities that are optimized to capture the social relationships between users and communities, and we use these embeddings to give our LSTM model information about the social context in which it is making predictions.
Our approach outperforms a number of strong baselines and could be used to create a 'raid' early-warning system for moderators to inform them of a potential impending influx of toxic users.
All the code necessary to replicate our prediction results is available at on GitHub.
The training and testing data can be downloaded here
Srijan Kumar, William L. Hamilton, Jure Leskovec, and Dan Jurafsky.
Community Interaction and Conflict on the Web.
The Web Conference (WWW). 2018.
Srijan can be reached at srijan at stanford.edu and Will can be reached at wleif at stanford.edu.
You can reach all of us at our institutional email addresses, given in the paper (i.e., it is our stanford.edu email addresses).
You can reach/follow us on Twitter as well: Srijan, William, Jure, and Dan.