Table 1.
Proportions of bot and human users in the training data.
Fig 1.
Online human-bot interactions during the Italian elections.
(a): Volumes of human-bot interactions in Twitter. (b-d): Human-bot interactions stratified by actions: Mentions, Replies and Retweets. (e): Geographic location of involved users, where the color encodes the number of tweets per country, in logarithmic scale. As in (a), humans are in red and bots are in blue. Users are mostly located in Italy, with relevant interactions from other countries worldwide. (e): Evolution across time of the overall social activity of humans and bots (top), also stratified by actions (bottom).
Fig 2.
Information cascades during Italian elections.
a) Heatmap of the number of users initiating information cascades, as a function of the size of their social neighborhood (Followers) and the size of the generated cascade; b) Scatter plot of the same data, with points encoding users. Color encodes bot/human classification and size encodes cascade’s diameter; c) As in a) but considering cascade rate (in units of retweets/hour), defined by the ratio between cascade size and its duration, vs. neighborhood size (left panels) and cascade size (right panels), for humans (top panels) and bots (bottom panels). The heatmap of cascade rate vs. neighborhood size allows one to identify 4 categories: hidden influentials, influentials, common users and broadcasters (see the text for further detail). Dashed lines indicate medians of structural and dynamical features in humans. Only cascades with at least 10 adopters are considered and, for heatmaps, the logarithm of the corresponding variables is considered.
Fig 3.
Social bulk of Italian elections.
a) Twitter users can retweet or mention or reply with each other. Each action encode a specific social meaning and, by considering the co-existence of endorsement (i.e. retweet) and discussion (i.e. mention or reply), between the same pairs of users, we filter out spurious interactions to identify the social bulk of the system. b) Visualization of the social bulk emerged during Italian elections, with users (i.e., the nodes) colored by the community they belong to (see the text for further detail). c) The eight communities with at least 2% of users are represented separately, while preserving their relative position in the social bulk shown in panel b). Note the remarkable star-like topology C8 characterizing the augmented human identified in the system.
Fig 4.
The online system is characterized by social fragmentation.
Top: Fragmentation encodes the tendency of online users to organize in multiple opposing groups (see the text for further detail). During the four considered periods, the online social network is fragmented much more than random expectation. Small changes in fragmentation of the observed system across time are reflected in the null model, indicating that they can be explained by small changes in the heterogeneity of the underlying connectivity. Error bars indicate standard deviations.
Table 2.
Most populated communities (with more than 250 users) in the social bulk, with top influencers listed per group. Top influencers are identified as hubs in the bulk network. As evident from the similarities among top influencers, groups reflect specific ecosystems of the Italian voting event: “Movimento 5 Stelle” (M5S), traditional media (Media), media with massive online presence (Web Media), “Partito Democratico” (PD), “Liberi e Uguali” (LEU), “Forza Italia” (FI), “Lega” and “Fratelli d’Italia” (Lega and FdI), and then the augmented human with all his/her interacting bots (Augmented Human). Bot (augmented) infiltration indicates the percentage of bot users (augmented humans) in each group. Excluding the community corresponding to the augmented human (made for 97.9% of bots), the mean bot infiltration in the bulk network is 29.2% while the mean augmented infiltration is 15.7%. The media groups are richer in bots as expected, since they include news media and online accounts of news papers. Note that users not corresponding to public groups, public entities or individuals with a public political profile (e.g., elected for a specific political party) have been anonymized. Interestingly, the account of User09 has been later suspended by Twitter for violating its policies.
Table 3.
Network analysis of groups in the social bulk reflect election outcomes.
The five political ecosystems from the bulk network are ranked against their topological features: i) interaction volume, i.e. the number of social actions within the group; ii) size, i.e. the number of individuals in the group. The rank based on online interactions strongly mirrors the election outcome (Spearman ρ = 0.9, p-value = 0.039), supporting the hypothesis that online social interactions are tightly entwined to outcomes and events in the real-world.