arXiv subjects

Diogo Pacheco

Publications and source records attributed to Diogo Pacheco.

15 recordsLinked to original sources

arXiv2026

How neighbourhood ideology shapes misinformation belief in densely tied social networks

With the rapid spread of news on social media, understanding the propagation of misinformation is becoming increasingly important. One factor that affects individuals' vulnerability to false information is their ideological predisposition. Despite the large number of agent-based models that focus on social influence as a driver of the spread of false claims, they often fail to explicitly integrate personal ideological biases into belief formation. In this work, we explore how misinformation spreads through the interaction between individuals' ideological biases and social influence. Our model accounts for both the strength of individuals' ideological biases and the extent to which a false claim aligns with their ideology. Social influence modifies the effects of ideological intensity and false claim alignment through network interactions. Notably, the influence of neighbours' ideological intensity on belief is strongly affected by how well those neighbours are connected to one another. These results highlight the importance of considering both network structure and personal ideological biases when modelling misinformation propagation.

cs.SI↗

arXiv2025

Kicking Politics: How Football Fan Communities Became Arenas for Political Influence

This paper investigates how political campaigns engaged UK football fan communities on Twitter in the aftermath of the Brexit Referendum (2016-2017). Football fandom, with its strong collective identities and tribal behaviours, offers fertile ground for political influence. Combining social network and content analysis, we examine how political discourse became embedded in football conversations. We show that a wide range of actors -- including parties, media, activist groups, and pseudonymous influencers -- mobilised support, provoked reactions, and shaped opinion within these communities. Through case studies of hashtag hijacking, embedded activism, and political "megaphones", we illustrate how campaigns leveraged fan cultures to amplify political messages. Our findings highlight mechanisms of political influence in ostensibly non-political online spaces and point toward the development of a broader framework in future work.

cs.SI↗

arXiv2025

Echoes of Automation: How Bots Shaped Political Discourse in Brazil

In an era where social media platforms are central to political communication, the activity of bots raises pressing concerns about amplification, manipulation, and misinformation. Drawing on more than 315 million tweets posted from August 2018 to June 2022, we examine behavioural patterns, sentiment dynamics, and the thematic focus of bot- versus human-generated content spanning the 2018 Brazilian presidential election and the lead-up to the 2022 contest. Our analysis shows that bots relied disproportionately on retweets and replies, with reply activity spiking after the 2018 election, suggesting tactics of conversational infiltration and amplification. Sentiment analysis indicates that bots maintained a narrower emotional tone, in contrast to humans, whose sentiment fluctuated more strongly with political events. Topic modelling further reveals bots' repetitive, Bolsonaro-centric messaging, while human users engaged with a broader range of candidates, civic concerns, and personal reflections. These findings underscore bots' role as amplifiers of narrow agendas and their potential to distort online political discourse.

cs.SI↗

arXiv2025

Super-Linear Growth and Rising Inequality in Online Social Communities: Insights from Reddit

We study the effect of the number of users on the activity of communities within the online content sharing and discussion platform Reddit, called subreddits. We found that comment activity on Reddit has a heavy-tailed distribution, where a large fraction of the comments are made by a small set of users. Furthermore, as subreddits grow in size, this behavior becomes stronger, with activity (measured by the comments made in a subreddit) becoming even more centralised in a (relatively) smaller core of users. We verify that these changes are not explained by finite size nor by sampling effects. Instead, we observe a systematic change of the distribution with subreddit size. To quantify the centralisation and inequality of activity in a subreddit, we used the Gini coefficient. We found that as subreddits grow in users, so does the Gini coefficient, seemingly as a natural effect of the scaling. We found that the excess number of comments (the total number of comments minus the total number of users) follows a power law with exponent 1.27. For each subreddit we considered a snapshot of one month of data, as a compromise between statistical relevance and change in the system's dynamics. We show results over the whole year 2021 (with each subreddit having twelve snapshots, at most), nevertheless all results were consistent when using a single month or different years.

physics.soc-ph↗

arXiv2024

Unveiling social vibrancy in urban spaces with app usage

Urban vibrancy is an important measure of the energetic nature of a city that is related to why and how people use urban spaces, and it is inherently connected with our social behaviour. Increasingly, people use a wide range of mobile phone apps in their daily lives to connect socially, search for information, make decisions, and arrange travel, amongst many other reasons. However, the relationship between online app usage and urban vibrancy remains unclear, particularly regarding how sociospatial behaviours interact with urban features. Here, we use app-usage data as a digital signature to investigate this question. To do this, we use a high-resolution data source of mobile service-level traffic volumes across eighteen cities in France. We investigate the social component of cities using socially relevant urban features constructed from OpenStreetMap 'Points of Interest'. We developed a methodology for identifying and classifying multidimensional app usage time series based on similarity. We used these in predictive models to interpret the results for each city and across France. Across cities, there were spatial behavioural archetypes, characterised by multidimensional properties. We found patterns between the week and the weekend, and across cities, and the country. These archetypes correspond to changes in socially relevant urban features that impact urban vibrancy. Our results add further evidence for the importance of using computational approaches to understand urban environments, the use of sociological concepts in computational science, and urban vibrancy in cities.

cs.CY↗

arXiv2024

Misinformation Dissemination: Effects of Network Density in Segregated Communities

Understanding the relationship between network features and misinformation propagation is crucial for mitigating the spread of false information. Here, we investigate how network density and segregation affect the dissemination of misinformation using a susceptible-infectious-recovered framework. We find that a higher density consistently increases the proportion of misinformation believers. In segregated networks, our results reveal that minorities affect the majority: denser minority groups increase the number of believers in the majority, demonstrating how the structure of a segregated minority can influence misinformation dynamics within the majority group.

cs.SI↗

arXiv2024

How the cascade inference problem distorts information diffusion

To analyze the flow of information online, experts often rely on platform-provided data from social media companies, which typically attribute all resharing actions to an original poster. This obscures the true dynamics of how information spreads online, as users can be exposed to content in various ways. While most researchers analyze data as it is provided by the platform and overlook this issue, some attempt to infer the structure of information cascades. However, the absence of ground truth about actual diffusion cascades makes it impossible to verify the efficacy of these efforts. We propose a novel parametric reconstruction approach and use it to investigate how overlooking cascade reconstruction distorts analyses of social influence, community detection, and information diffusion. Two case studies involving data from Twitter and Bluesky reveal that cascade inference significantly impacts the identification of both influential users and communities, therefore affecting downstream analyses in general. Analysis of the diffusion of over 40,000 true and false news stories on Twitter reveals that the assumptions made during the reconstruction procedure drastically distort both microscopic and macroscopic properties of cascade networks. This work highlights the challenges of studying information spreading processes on complex networks and has significant implications for the broader study of digital platforms.

cs.SI↗

arXiv2024

Identifying and characterizing superspreaders of low-credibility content on Twitter

The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content -- so-called superspreaders -- are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.

cs.SI↗

arXiv2023

Bots, Elections, and Controversies: Twitter Insights from Brazil's Polarised Elections

From 2018 to 2023, Brazil experienced its most fiercely contested elections in history, resulting in the election of far-right candidate Jair Bolsonaro followed by the left-wing, Lula da Silva. This period was marked by a murder attempt, a coup attempt, the pandemic, and a plethora of conspiracy theories and controversies. This paper analyses 437 million tweets originating from 13 million accounts associated with Brazilian politics during these two presidential election cycles. We focus on accounts' behavioural patterns. We noted a quasi-monotonic escalation in bot engagement, marked by notable surges both during COVID-19 and in the aftermath of the 2022 election. The data revealed a strong correlation between bot engagement and the number of replies during a single day ($r=0.66$, $p<0.01$). Furthermore, we identified a range of suspicious activities, including an unusually high number of accounts being created on the same day, with some days witnessing over 20,000 new accounts and super-prolific accounts generating close to 100,000 tweets. Lastly, we uncovered a sprawling network of accounts sharing Twitter handles, with a select few managing to utilise more than 100 distinct handles. This work can be instrumental in dismantling coordinated campaigns and offer valuable insights for the enhancement of bot detection algorithms.

cs.SI↗

arXiv2023

Dynamic predictability and spatio-temporal contexts in human mobility

Human travelling behaviours are markedly regular, to a large extent, predictable, and mostly driven by biological necessities (\eg sleeping, eating) and social constructs (\eg school schedules, synchronisation of labour). Not surprisingly, such predictability is influenced by an array of factors ranging in scale from individual (\eg preference, choices) and social (\eg household, groups) all the way to global scale (\eg mobility restrictions in a pandemic). In this work, we explore how spatio-temporal patterns in individual-level mobility, which we refer to as \emph{predictability states}, carry a large degree of information regarding the nature of the regularities in mobility. Our findings indicate the existence of contextual and activity signatures in predictability states, pointing towards the potential for more sophisticated, data-driven approaches to short-term, higher-order mobility predictions beyond frequentist/probabilistic methods.

physics.soc-ph↗

arXiv2023

Betting the system: Using lineups to predict football scores

This paper aims to reduce randomness in football by analysing the role of lineups in final scores using machine learning prediction models we have developed. Football clubs invest millions of dollars on lineups and knowing how individual statistics translate to better outcomes can optimise investments. Moreover, sports betting is growing exponentially and being able to predict the future is profitable and desirable. We use machine learning models and historical player data from English Premier League (2020-2022) to predict scores and to understand how individual performance can improve the outcome of a match. We compared different prediction techniques to maximise the possibility of finding useful models. We created heuristic and machine learning models predicting football scores to compare different techniques. We used different sets of features and shown goalkeepers stats are more important than attackers stats to predict goals scored. We applied a broad evaluation process to assess the efficacy of the models in real world applications. We managed to predict correctly all relegated teams after forecast 100 consecutive matches. We show that Support Vector Regression outperformed other techniques predicting final scores and that lineups do not improve predictions. Finally, our model was profitable (42% return) when emulating a betting system using real world odds data.

cs.LG↗

arXiv2021

Neutral bots probe political bias on social media

Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal accounts are exposed to moderate content shifting their experience toward the political center. Partisan accounts, especially conservative ones, tend to receive more followers and follow more automated accounts. Conservative accounts also find themselves in denser communities and are exposed to more low-credibility content.

cs.SI↗

arXiv2021

Uncovering Coordinated Networks on Social Media: Methods and Case Studies

Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of these cases, we detect networks of coordinated Twitter accounts by examining their identities, images, hashtag sequences, retweets, or temporal patterns. The proposed approach proves to be broadly applicable to uncover different kinds of coordination across information warfare scenarios.

cs.SI↗

arXiv2020

Unveiling Coordinated Groups Behind White Helmets Disinformation

Propaganda, disinformation, manipulation, and polarization are the modern illnesses of a society increasingly dependent on social media as a source of news. In this paper, we explore the disinformation campaign, sponsored by Russia and allies, against the Syria Civil Defense (a.k.a. the White Helmets). We unveil coordinated groups using automatic retweets and content duplication to promote narratives and/or accounts. The results also reveal distinct promoting strategies, ranging from the small groups sharing the exact same text repeatedly, to complex "news website factories" where dozens of accounts synchronously spread the same news from multiple sites.

cs.SI↗

arXiv2019

Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem

Simulating and predicting planetary-scale techno-social systems poses heavy computational and modeling challenges. The DARPA SocialSim program set the challenge to model the evolution of GitHub, a large collaborative software-development ecosystem, using massive multi-agent simulations. We describe our best performing models and our agent-based simulation framework, which we are currently extending to allow simulating other planetary-scale techno-social systems. The challenge problem measured participant's ability, given 30 months of meta-data on user activity on GitHub, to predict the next months' activity as measured by a broad range of metrics applied to ground truth, using agent-based simulation. The challenge required scaling to a simulation of roughly 3 million agents producing a combined 30 million actions, acting on 6 million repositories with commodity hardware. It was also important to use the data optimally to predict the agent's next moves. We describe the agent framework and the data analysis employed by one of the winning teams in the challenge. Six different agent models were tested based on a variety of machine learning and statistical methods. While no single method proved the most accurate on every metric, the broadly most successful sampled from a stationary probability distribution of actions and repositories for each agent. Two reasons for the success of these agents were their use of a distinct characterization of each agent, and that GitHub users change their behavior relatively slowly.

cs.MA↗