Communities, Gateways, and Bridges: Measuring Attention Flow in the Reddit Political Sphere

Abstract

Online social media have attracted large and vibrant communities, which shape how people interact online. Platforms such as Reddit provide a safe harbor for groups to discuss a variety of topics, including politics and even conspiracy theories. We propose a framework, dubbed attention-flow graph, to investigate the flow of users across Reddit communities from a network perspective. This graph concisely summarizes how users shift their attention from one subreddit to another over time, and allows to capture its community structure. In addition, it enables the operationalization of the concepts of gateways and bridges: particular subreddits that support the transition of users towards specific communities. We apply this framework to identify political and conspiracy communities, thus discovering their bridges and gateways. We find that conspiracy theories help attracting users to the alt-right community from occultist subreddits, but also by diverting users from the radical left.

Appendices

Appendix

A Weight Rescaling

Fig. 4.

Distribution of the subreddits in the space defined by their number of monthly users and their (rescaled) node strength in the attention-flow graph, i.e., the sum of the weight of all of their links.

In order to define the weights of the links in the attention-flow graphs, we wish to focus on links that are deviating from the most popular subreddits. E.g., the constant flow of users towards a generalist subreddit such as r/pictures is not as interesting for our purposes as the flow between two topical communities. For this reason, we rescale the weight w of a link between two subreddits $(s, s')$ in order to reduce the correlation of w with the number of users of s and $s'$. Thanks to our framework, we are able to perform such normalization using only information coming from the node weights themselves. In fact, we rescale each weight by the mean of the total weights of the links incident on the head and the tail of each link. We compare two types of means: arithmetic and geometric. Figure 4 reports this comparison, clearly showing that the geometric mean succeeds in this goal, as it dampens the dependency between the resulting subreddit node strength (i.e., the sum of the weights w of all of their links in the attention flow graph) and the number of monthly users in each subreddit.

B Pruning Thresholds

As shown in table Table 1, community detection algorithm benefits from edge pruning. In fact, the weights distribution of the attention-flow graph, plotted in Fig. 5, spans over several orders of magnitudes and the considerable amount of lightly weighted links adds a background noise in the network, which is detrimental for the community detection.

Fig. 5.

Weights distribution of the attention-flow graph, with the investigated pruning thresholds at $25^{th}$, $50^{th}$ and $75^{th}$ percentiles indicated by red vertical lines. (Color figure online)

C Mentioned Communities

In Table 4, we report the top-10 largest subreddits by userbase for all the mentioned communities: the four politicized communities of interest, and their neighboring communities mentioned in Table 5.

Table 4. Top-10 subreddits by userbase for mentioned communities.

Table 5. Top-5 communities by normalized inflow for three gateway nodes.