The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset

The discourse around conspiracy theories is currently thriving amidst the rampant misinformation prevalent in online environments. Research in this field has been focused on detecting conspiracy theories on social media, often relying on limited datasets. In this study, we present a novel methodology for constructing a Twitter dataset that encompasses accounts engaged in conspiracy-related activities throughout the year 2022. Our approach centers on data collection that is independent of specific conspiracy theories and information operations. Additionally, our dataset includes a control group comprising randomly selected users who can be fairly compared to the individuals involved in conspiracy activities. This comprehensive collection effort yielded a total of 15K accounts and 37M tweets extracted from their timelines. We conduct a comparative analysis of the two groups across three dimensions: topics, profiles, and behavioral characteristics. The results indicate that conspiracy and control users exhibit similarity in terms of their profile metadata characteristics. However, they diverge significantly in terms of behavior and activity, particularly regarding the discussed topics, the terminology used, and their stance on trending subjects. Interestingly, there is no significant disparity in the presence of bot users between the two groups, suggesting that conspiracy and automation are orthogonal concepts. Finally, we develop a classifier to identify conspiracy users using 93 features, some of which are commonly employed in literature for troll identification. The results demonstrate a high accuracy level (with an average F1 score of 0.98%), enabling us to uncover the most discriminative features associated with conspiracy-related accounts.

The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-modal Distillation

Trends and opinion mining in social media increasingly focus on novel interactions involving visual media, like images and short videos, in addition to text. In this work, we tackle the problem of visual sentiment analysis of social media images -- specifically, the prediction of image sentiment polarity. While previous work relied on manually labeled training sets, we propose an automated approach for building sentiment polarity classifiers based on a cross-modal distillation paradigm; starting from scraped multimodal (text + images) data, we train a student model on the visual modality based on the outputs of a textual teacher model that analyses the sentiment of the corresponding textual modality. We applied our method to randomly collected images crawled from Twitter over three months and produced, after automatic cleaning, a weakly-labeled dataset of $\sim$1.5 million images. Despite exploiting noisy labeled samples, our training pipeline produces classifiers showing strong generalization capabilities and outperforming the current state of the art on five manually labeled benchmarks for image sentiment polarity prediction.

Demystifying Misconceptions in Social Bots Research

The science of social bots seeks knowledge and solutions to one of the most debated forms of online misinformation. Yet, social bots research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. In this contribution we revise some recent results in social bots research, highlighting and correcting factual errors as well as methodological and conceptual issues. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss misinformation research in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research as well as providing indications on the correct methodologies and sound directions for future research in the field.

Modularity-based approach for tracking communities in dynamic social networks

Community detection is a fundamental task in social network analysis. Online social networks have dramatically increased the volume and speed of interactions among users, enabling advanced analysis of these dynamics. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, most community detection efforts focus on communities within static networks. Here, we describe a framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. To this end, a modularity-based strategy is proposed to effectively detect and track dynamic communities. The potential of our framework is shown by conducting extensive experiments on synthetic networks containing embedded events. Results indicate that our framework outperforms other state-of-the-art methods. In addition, we briefly explore how the proposed approach can identify dynamic communities in a Twitter network composed of more than 60,000 users, which posted over 5 million tweets throughout 2020. The proposed framework can be applied to different social network and provides a valuable tool to understand the evolution of communities in dynamic social networks.

Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out the first dynamic analysis of coordinated behavior. To reach our goal we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. Thanks to our novel approach we find that: (i) coordinated communities feature variable degrees of temporal instability; (ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; (iii) some users exhibit distinct archetypal behaviors that have important practical implications; (iv) content and network characteristics contribute to explaining why users leave and join coordinated communities. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of coordinated communities, and on the patterns of online influence.

MulBot: Unsupervised Bot Detection Based on Multivariate Time Series

Online social networks are actively involved in the removal of malicious social bots due to their role in the spread of low quality information. However, most of the existing bot detectors are supervised classifiers incapable of capturing the evolving behavior of sophisticated bots. Here we propose MulBot, an unsupervised bot detector based on multivariate time series (MTS). For the first time, we exploit multidimensional temporal features extracted from user timelines. We manage the multidimensionality with an LSTM autoencoder, which projects the MTS in a suitable latent space. Then, we perform a clustering step on this encoded representation to identify dense groups of very similar users -- a known sign of automation. Finally, we perform a binary classification task achieving f1-score $= 0.99$, outperforming state-of-the-art methods (f1-score $\le 0.97$). Not only does MulBot achieve excellent results in the binary classification task, but we also demonstrate its strengths in a novel and practically-relevant task: detecting and separating different botnets. In this multi-class classification task we achieve f1-score $= 0.96$. We conclude by estimating the importance of the different features used in our model and by evaluating MulBot's capability to generalize to new unseen bots, thus proposing a solution to the generalization deficiencies of supervised bot detectors.

Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets

In the last years there has been a growing attention towards predicting the political orientation of active social media users, being this of great help to study political forecasts, opinion dynamics modeling and users polarization. Existing approaches, mainly targeting Twitter users, rely on content-based analysis or are based on a mixture of content, network and communication analysis. The recent research perspective exploits the fact that a user's political affinity mainly depends on his/her positions on major political and social issues, thus shifting the focus on detecting the stance of users through user-generated content shared on social networks. The work herein described focuses on a completely unsupervised stance detection framework that predicts the user's stance about specific social-political statements by exploiting content-based analysis of its Twitter timeline. The ground-truth user's stance may come from Voting Advice Applications, online tools that help citizens to identify their political leanings by comparing their political preferences with party political stances. Starting from the knowledge of the agreement level of six parties on 20 different statements, the objective of the study is to predict the stance of a Party p in regard to each statement s exploiting what the Twitter Party account wrote on Twitter. To this end we propose Tweets2Stance (T2S), a novel and totally unsupervised stance detector framework which relies on the zero-shot learning technique to quickly and accurately operate on non-labeled data. Interestingly, T2S can be applied to any social media user for any context of interest, not limited to the political one. Results obtained from multiple experiments show that, although the general maximum F1 value is 0.4, T2S can correctly predict the stance with a general minimum MAE of 1.13, which is a great achievement considering the task complexity.

TweepFake: about Detecting Deepfake Tweets

The threat of deepfakes, synthetic, or manipulated media, is becoming increasingly alarming, especially for social media platforms that have already been accused of manipulating public opinion. Even the cheapest text generation techniques (e.g. the search-and-replace method) can deceive humans, as the Net Neutrality scandal proved in 2017. Meanwhile, more powerful generative models have been released, from RNN-based methods to the GPT-2 language model. State-of-the-art language models, transformer-based in particular, can generate synthetic text in response to the model being primed with arbitrary input. Thus, Therefore, it is crucial to develop tools that help to detect media authenticity. To help the research in this field, we collected a dataset of real Deepfake tweets. It is real in the sense that each deepfake tweet was actually posted on Twitter. We collected tweets from a total of 23 bots, imitating 17 human accounts. The bots are based on various generation techniques, i.e., Markov Chains, RNN, RNN+Markov, LSTM, GPT-2. We also randomly selected tweets from the humans imitated by the bots to have an overall balanced dataset of 25,836 tweets (half human and half bots generated). The dataset is publicly available on Kaggle. In order to create a solid baseline for detection techniques on the proposed dataset we tested 13 detection methods based on various state-of-the-art approaches. The detection results reported as a baseline using 13 detection methods, confirm that the newest and more sophisticated generative methods based on transformer architecture (e.g., GPT-2) can produce high-quality short texts, difficult to detect.

Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter

Within OSNs, many of our supposedly online friends may instead be fake accounts called social bots, part of large groups that purposely re-share targeted content. Here, we study retweeting behaviors on Twitter, with the ultimate goal of detecting retweeting social bots. We collect a dataset of 10M retweets. We design a novel visualization that we leverage to highlight benign and malicious patterns of retweeting activity. In this way, we uncover a 'normal' retweeting pattern that is peculiar of human-operated accounts, and 3 suspicious patterns related to bot activities. Then, we propose a bot detection technique that stems from the previous exploration of retweeting behaviors. Our technique, called Retweet-Buster (RTbust), leverages unsupervised feature extraction and clustering. An LSTM autoencoder converts the retweet time series into compact and informative latent feature vectors, which are then clustered with a hierarchical density-based algorithm. Accounts belonging to large clusters characterized by malicious retweeting patterns are labeled as bots. RTbust obtains excellent detection results, with F1 = 0.87, whereas competitors achieve F1 < 0.76. Finally, we apply RTbust to a large dataset of retweets, uncovering 2 previously unknown active botnets with hundreds of accounts.