Insights into using temporal coordinated behaviour to explore connections between social media posts and influence (2025)

Abstract: Political campaigns increasingly rely on targeted strategies to influence voters on social media. Often, such campaigns have been studied by analysing coordinated behaviour to identify communities of users who exhibit similar patterns. While these analyses are typically conducted on static networks, recent extensions to temporal networks allow tracking users who change communities over time, opening new opportunities to quantitatively study influence in social networks. As a first step toward this goal, we analyse the messages users were exposed to during the UK 2019 election, comparing those received by users who shifted communities with others covering the same topics.Our findings reveal 54 statistically significant linguistic differences and show that a subset of persuasion techniques, including loaded language, exaggeration and minimization, doubt, and flag-waving, are particularly relevant to users’ shifts. This work underscores the importance of analysing coordination from a temporal and dynamic perspective to infer the drivers of users’ shifts in online debate.

## introduction the unprecedented rise in social media usage creates the opportunity to share ideas and opinions with an ever-broader audience. this is particularly impactful in the political context, as demonstrated by the central role of social media in political campaigns and by the widespread use of targeted digital strategies to influence and coordinate voters @xcite . several works study this phenomenon from different angles. social media content has been examined for its use of persuasion techniques @xcite and linguistic tricks @xcite , or through the network of user interactions @xcite , e.g., relying on retweets or hashtags, to identify coordination among users @xcite . the analysis of coordination yields clusters of users that allow to extrapolate information about them: @xcite show that the main groups identified correspond to supporters of political parties and activist groups during the uk 2019 elections. recently, @xcite extended the static analysis on coordination to the temporal dimension. in their work, they uncover different classes of user behaviour, which they map to archetypes. in particular, one of the archetypes, archetype 2, corresponds to users that change their original community and stay in the destination community for a relatively long time. this temporal behaviour is especially interesting, as "the shifts detected via dynamic analyses of coordination could contribute to identifying successful cases of influence over users or communities in a network" @xcite , therefore possibly providing an invaluable tool for quantitative studies of persuasion on social networks. as an initial step toward this goal, we first verify the quality of the dynamic communities identified in @xcite by using different types of user interactions to compute them, i.e. likes instead of retweets. then we analyse the messages that users who changed community (archetype 2) were exposed to, in order to detect linguistic cues related to persuasion. specifically, we tackle the following research questions: rq1 would interaction signals different than retweets and hashtags still yield comparable communities? rq2 are there significant differences between the messages that the users who have changed community (archetype 2) have been exposed to and other messages shared in the same time period? the contributions of the paper are the following: ## related works social media networks and user behaviour. in the literature, we find multiple works addressing different types of user behaviour and their relationship to influence, such as building a retweet network to analyse the influence of opinions on wind energy @xcite . in the context of politics, another form of user behaviour, coordination, has gained interest, as it is necessary for large-scale online campaigns. @xcite present a network-based framework that discovers coordination as a significant similarity between users by constructing a user similarity network. however, this method aggregates user activities and does not consider their variations over time. to close this gap, @xcite apply a dynamic community detection algorithm to identify groups of users with similar behaviours, and analyse their changes over time. in their analysis they describe two types of users, which they refer to as archetypes. specifically, archetype 1 or "stationary" users are the ones who do not change community in the period under consideration and archetype 2 users are the ones who change community and then remain in the destination community for a long time. related to our work is the paper from @xcite , which combines the analysis of static coordinated communities with the messages shared within them to identify the use of propaganda during the uk 2019 elections. however, besides not taking into account the temporal evolution of communities, it applies a classifier for propagandistic texts, while we focus on the analysis of messages to find linguistic cues related to persuasion. influence and social media content. da san @xcite propose a bert-based multigranularity model capable of identifying the pres-ence and location of 18 persuasion techniques, selected from those commonly present in political propaganda @xcite . the work of @xcite deals with shorter texts, analysing facebook posts from polish travel agencies. they investigate the presence of persuasive linguistic tricks which, however, are textual cues tailored to marketing and are not adaptable to the political context. a previous work from @xcite identified and measured the use of 49 potential context-independent deceptive language cues in tweets from fraudulent accounts. their work shows that these types of linguistic features can help discriminate troll accounts from authentic ones and may also be useful when addressing influence. ## dataset as our goal is to investigate the changes in the communities highlighted in the work of @xcite , we use the same dataset: the twitter 2019 uk election dataset, which was first presented in @xcite . from the 1m distinct users in the dataset, @xcite extract 12k superspreaders, i.e. the 1% of users with the highest number of retweets, and 3m tweets they shared, of which 441k are original content (i.e., not retweets). they construct a multiplex temporal network based on co-retweets. the network is built over overlapping time-windows of 7 days each, with a 1 day shift between them. then they apply the leiden community detection algorithm to identify and track the evolution of communities over time and observe the behaviour of superspreaders, i.e. whether they stay in the same community or change it at some point in time. the main communities found in the dataset are: lab1-labourist party, lab2-labourists with different temporal behaviours than lab1, rchlabourists spreading the manifesto and pushing others to vote, b60-users against the pension age equalization law, tvt-a group composed of multiple political parties militating for a tactical vote in favor of labourists, snp-users supporting the scottish national party, snpo-opposers to the scottish national party, con-conservative party, aseconservative party engaged in attacking the labour party, and brx-users in favor of brexit. ## dataset extension in the dataset, only 211 out of 12k superspreaders belong to archetype 2, not enough to present solid results. to overcome this, we extend the analysis to non-superspreader users (nonss), who had been excluded in the original work. to identify communities, @xcite used the leiden community detection algorithm, which identifies more densely connected groups, as such it cannot be applied to non-superspreaders, of whom only 6% made at least 20 retweets in the entire month of collection (see figure 1 shows lineplots of the average cosine distance between same, similar and dissimilar communities. the distance is computed with respect to the retweet vectors. considering vectors composed of all retweets made by the users, the average distances between same, similar and dissimilar communities appeared to fall within a very close range (see "all tweets" in figure 1 ), making a clear separation difficult. by checking the percentage of tweets in common between users of each community ( we also observe an overlap between the distance ranges of similar communities and same communities. this overlap is consistently given by the distances between communities rch, lab1 and lab2, which appear to be so close to each other that they result comparable with intra-community distances. as such, any assignment errors made between this three communities are negligible and the maximum of their distances is a good upper bound for the "slight" error category. let a be a user belonging to community x but assigned to community y, and be dist(c x , c y ) the distance between communities x and y, we classify assignment errors as follows: to measure the accuracy of our algorithm, we use stationary and archetype 2 superspreaders. since these classes have very distinct behaviours, they are ideal for evaluation. we assign a community to each stationary user using the rest of the stationary superspreaders, and assign each archetype 2 user using all stationary superspreaders. we decided to use the subset of tweets retweeted by at least 1/8 of the stationary community members and assign a user to the majority community among the 4 closest stationary superspreaders, as this provided the best trade-off between accuracy and errors, which can be seen in by applying the algorithm and definition of archetype 2 users to all nonss users who have at least one retweet for each time-window, we obtain a total of 8562 archetype 2 users. ## conclusion the temporal analysis of coordinated behaviour highlights users that changed community over time. this may open up new possibilities to quantitatively study influence in social networks. by analysing the patterns of likes usage, we provided additional evidence supporting the communities identified through temporal analysis. in addition, we analysed the messages that users who changed community have been exposed to, comparing them with random messages on the same topic. we found 54 statistically significant different linguistic features, as well as different use of some persuasion techniques, namely loaded language, exaggeration and minimisation, doubt, and flag-waving. however, there are many factors that can push a user to switch communities, and these can happen across multiple platforms or even outside the online sphere. as future work, we plan to set up an experiment with humans in a controlled environment to test whether messages with the characteristics we identified tend to be more persuasive than, for example, messages having other persuasion techniques or other linguistic features.