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Emilio Ferrara

Publications and source records attributed to Emilio Ferrara.

At least 109 records · Page 6Linked to original sources
arXiv2023

Parasocial diffusion: K-pop fandoms help drive COVID-19 public health messaging on social media

We examine an unexpected but significant source of positive public health messaging during the COVID-19 pandemic -- K-pop fandoms. Leveraging more than 7 million tweets related to mask-wearing and K-pop between March 2020 and December 2021, we analyzed the online spread of the hashtag \#WearAMask and vaccine-related tweets amid anti-mask sentiments and public health misinformation. Analyses reveal the South Korean boyband BTS as one of the most significant driver of health discourse. Tweets from health agencies and prominent figures that mentioned K-pop generate 111 times more online responses compared to tweets that did not. These tweets also elicited strong responses from South America, Southeast Asia, and rural States -- areas often neglected in Twitter-based messaging by mainstream social media campaigns. Network and temporal analysis show increased use from right-leaning elites over time. Mechanistically, strong-levels of parasocial engagement and connectedness allow sustained activism in the community. Our results suggest that public health institutions may leverage pre-existing audience markets to synergistically diffuse and target under-served communities both domestically and globally, especially during health crises such as COVID-19.

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arXiv2023

How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events

Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviours that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days from their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, often making unwarranted and excessive use of reply and mention features, and predominantly sharing spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings shed light on patterns of platform abuse and subsequent moderation during major events.

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arXiv2023

Controlled Text Generation with Hidden Representation Transformations

We propose CHRT (Control Hidden Representation Transformation) - a controlled language generation framework that steers large language models to generate text pertaining to certain attributes (such as toxicity). CHRT gains attribute control by modifying the hidden representation of the base model through learned transformations. We employ a contrastive-learning framework to learn these transformations that can be combined to gain multi-attribute control. The effectiveness of CHRT is experimentally shown by comparing it with seven baselines over three attributes. CHRT outperforms all the baselines in the task of detoxification, positive sentiment steering, and text simplification while minimizing the loss in linguistic qualities. Further, our approach has the lowest inference latency of only 0.01 seconds more than the base model, making it the most suitable for high-performance production environments. We open-source our code and release two novel datasets to further propel controlled language generation research.

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arXiv2023

Identifying Informational Sources in News Articles

News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We show that our dataset can be used to train high-performing models for information detection and source attribution. We further introduce a novel task, source prediction, to study the compositionality of sources in news articles. We show good performance on this task, which we argue is an important proof for narrative science exploring the internal structure of news articles and aiding in planning-based language generation, and an important step towards a source-recommendation system to aid journalists.

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arXiv2023

Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War Between Ukraine and Russia

On February 24, 2022, Russia invaded Ukraine. In the days that followed, reports kept flooding in from layman to news anchors of a conflict quickly escalating into war. Russia faced immediate backlash and condemnation from the world at large. While the war continues to contribute to an ongoing humanitarian and refugee crisis in Ukraine, a second battlefield has emerged in the online space, both in the use of social media to garner support for both sides of the conflict and also in the context of information warfare. In this paper, we present a collection of over 63 million tweets, from February 22, 2022 through March 8, 2022 that we are publishing for the wider research community to use. This dataset can be found at https://github.com/echen102/ukraine-russia and will be maintained and regularly updated as the war continues to unfold. Our preliminary analysis already shows evidence of public engagement with Russian state sponsored media and other domains that are known to push unreliable information; the former saw a spike in activity on the day of the Russian invasion. Our hope is that this public dataset can help the research community to further understand the ever evolving role that social media plays in information dissemination, influence campaigns, grassroots mobilization, and much more, during a time of conflict.

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arXiv2023

Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks

Estimating the political leanings of social media users is a challenging and ever more pressing problem given the increase in social media consumption. We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Retweet-BERT leverages the retweet network structure and the language used in users' profile descriptions. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies. Retweet-BERT demonstrates competitive performance against other state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter datasets (a COVID-19 dataset and a 2020 United States presidential elections dataset). We also perform manual validation to validate the performance of Retweet-BERT on users not in the training data. Finally, in a case study of COVID-19, we illustrate the presence of political echo chambers on Twitter and show that it exists primarily among right-leaning users. Our code is open-sourced and our data is publicly available.

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arXiv2023

Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter

Social media provide a fertile ground where conspiracy theories and radical ideas can flourish, reach broad audiences, and sometimes lead to hate or violence beyond the online world itself. QAnon represents a notable example of a political conspiracy that started out on social media but turned mainstream, in part due to public endorsement by influential political figures. Nowadays, QAnon conspiracies often appear in the news, are part of political rhetoric, and are espoused by significant swaths of people in the United States. It is therefore crucial to understand how such a conspiracy took root online, and what led so many social media users to adopt its ideas. In this work, we propose a framework that exploits both social interaction and content signals to uncover evidence of user radicalization or support for QAnon. Leveraging a large dataset of 240M tweets collected in the run-up to the 2020 US Presidential election, we define and validate a multivariate metric of radicalization. We use that to separate users in distinct, naturally-emerging, classes of behaviors associated to radicalization processes, from self-declared QAnon supporters to hyper-active conspiracy promoters. We also analyze the impact of Twitter's moderation policies on the interactions among different classes: we discover aspects of moderation that succeed, yielding a substantial reduction in the endorsement received by hyper-active QAnon accounts. But we also uncover where moderation fails, showing how QAnon content amplifiers are not deterred or affected by Twitter intervention. Our findings refine our understanding of online radicalization processes, reveal effective and ineffective aspects of moderation, and call for the need to further investigate the role social media play in the spread of conspiracies.

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arXiv2023

The Interconnected Nature of Online Harm and Moderation: Investigating the Cross-Platform Spread of Harmful Content between YouTube and Twitter

The proliferation of harmful content shared online poses a threat to online information integrity and the integrity of discussion across platforms. Despite various moderation interventions adopted by social media platforms, researchers and policymakers are calling for holistic solutions. This study explores how a target platform could leverage content that has been deemed harmful on a source platform by investigating the behavior and characteristics of Twitter users responsible for sharing moderated YouTube videos. Using a large-scale dataset of 600M tweets related to the 2020 U.S. election, we find that moderated Youtube videos are extensively shared on Twitter and that users who share these videos also endorse extreme and conspiratorial ideologies. A fraction of these users are eventually suspended by Twitter, but they do not appear to be involved in state-backed information operations. The findings of this study highlight the complex and interconnected nature of harmful cross-platform information diffusion, raising the need for cross-platform moderation strategies.

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arXiv2023

Leveraging Social Interactions to Detect Misinformation on Social Media

Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as reliable or unreliable, based on a previous evaluation of the information source. The models identifying unreliable threads usually rely on textual features. But reliability is not just what is said, but by whom and to whom. We additionally leverage on network information. Following the homophily principle, we hypothesize that users who interact are generally interested in similar topics and spreading similar kind of news, which in turn is generally reliable or not. We test several methods to learn representations of the social interactions within the cascades, combining them with deep neural language models in a Multi-Input (MI) framework. Keeping track of the sequence of the interactions during the time, we improve over previous state-of-the-art models.

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arXiv2023

Unveiling the Dynamics of Censorship, COVID-19 Regulations, and Protest: An Empirical Study of Chinese Subreddit r/china_irl

The COVID-19 pandemic has intensified numerous social issues that warrant academic investigation. Although information dissemination has been extensively studied, the silenced voices and censored content also merit attention due to their role in mobilizing social movements. In this paper, we provide empirical evidence to explore the relationships among COVID-19 regulations, censorship, and protest through a series of social incidents occurred in China during 2022. We analyze the similarities and differences between censored articles and discussions on r/china\_irl, the most popular Chinese-speaking subreddit, and scrutinize the temporal dynamics of government censorship activities and their impact on user engagement within the subreddit. Furthermore, we examine users' linguistic patterns under the influence of a censorship-driven environment. Our findings reveal patterns in topic recurrence, the complex interplay between censorship activities, user subscription, and collective commenting behavior, as well as potential linguistic adaptation strategies to circumvent censorship. These insights hold significant implications for researchers interested in understanding the survival mechanisms of marginalized groups within censored information ecosystems.

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arXiv2023

Zero-shot meta-learning for small-scale data from human subjects

While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled. Existing methods applied to such data often do not easily generalize to out-of-sample subjects. Instead, models must make predictions on test data that may be drawn from a different distribution, a problem known as \textit{zero-shot learning}. To address this challenge, we develop an end-to-end framework using a meta-learning approach, which enables the model to rapidly adapt to a new prediction task with limited training data for out-of-sample test data. We use three real-world small-scale human subjects datasets (two randomized control studies and one observational study), for which we predict treatment outcomes for held-out treatment groups. Our model learns the latent treatment effects of each intervention and, by design, can naturally handle multi-task predictions. We show that our model performs the best holistically for each held-out group and especially when the test group is distinctly different from the training group. Our model has implications for improved generalization of small-size human studies to the wider population.

cs.LG↗
arXiv2023

Retrieving false claims on Twitter during the Russia-Ukraine conflict

Nowadays, false and unverified information on social media sway individuals' perceptions during major geo-political events and threaten the quality of the whole digital information ecosystem. Since the Russian invasion of Ukraine, several fact-checking organizations have been actively involved in verifying stories related to the conflict that circulated online. In this paper, we leverage a public repository of fact-checked claims to build a methodological framework for automatically identifying false and unsubstantiated claims spreading on Twitter in February 2022. Our framework consists of two sequential models: First, the claim detection model identifies whether tweets incorporate a (false) claim among those considered in our collection. Then, the claim retrieval model matches the tweets with fact-checked information by ranking verified claims according to their relevance with the input tweet. Both models are based on pre-trained language models and fine-tuned to perform a text classification task and an information retrieval task, respectively. In particular, to validate the effectiveness of our methodology, we consider 83 verified false claims that spread on Twitter during the first week of the invasion, and manually annotate 5,872 tweets according to the claim(s) they report. Our experiments show that our proposed methodology outperforms standard baselines for both claim detection and claim retrieval. Overall, our results highlight how social media providers could effectively leverage semi-automated approaches to identify, track, and eventually moderate false information that spreads on their platforms.

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arXiv2023

Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine

Online social media represent an oftentimes unique source of information, and having access to reliable and unbiased content is crucial, especially during crises and contentious events. We study the spread of propaganda and misinformation that circulated on Facebook and Twitter during the first few months of the Russia-Ukraine conflict. By leveraging two large datasets of millions of social media posts, we estimate the prevalence of Russian propaganda and low-credibility content on the two platforms, describing temporal patterns and highlighting the disproportionate role played by superspreaders in amplifying unreliable content. We infer the political leaning of Facebook pages and Twitter users sharing propaganda and misinformation, and observe they tend to be more right-leaning than the average. By estimating the amount of content moderated by the two platforms, we show that only about 8-15% of the posts and tweets sharing links to Russian propaganda or untrustworthy sources were removed. Overall, our findings show that Facebook and Twitter are still vulnerable to abuse, especially during crises: we highlight the need to urgently address this issue to preserve the integrity of online conversations.

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arXiv2023

Detecting Social Media Manipulation in Low-Resource Languages

Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter's crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models, including BERT, and yields marked advantages in settings with very limited training data -- the norm when dealing with detecting malicious activity in online platforms.

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arXiv2023

Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on Twitter

The increasing prevalence of location-sharing features on social media has enabled researchers to ground computational social science research using geolocated data, affording opportunities to study human mobility, the impact of real-world events, and more. This paper analyzes what crucially separates posts with geotags from those without. We find that users who share location are not representative of the social media user population at large, jeopardizing the generalizability of research that uses only geolocated data.We consider three aspects: affect -- sentiment and emotions, content -- textual and non-textual, and audience engagement. By comparing a dataset of 1.3 million geotagged tweets with a random dataset of the same size, we show that geotagged posts on Twitter exhibit significantly more positivity, are often about joyous and special events such as weddings or graduations, convey more collectivism rather than individualism, and contain more additional features such as hashtags or objects in images, but at the same time generate substantially less engagement. These findings suggest there exist significant differences in the messages conveyed in geotagged posts. Our research carries important implications for future research utilizing geolocation social media data.

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arXiv2023

Twitter Spam and False Accounts Prevalence, Detection and Characterization: A Survey

The issue of quantifying and characterizing various forms of social media manipulation and abuse has been at the forefront of the computational social science research community for over a decade. In this paper, I provide a (non-comprehensive) survey of research efforts aimed at estimating the prevalence of spam and false accounts on Twitter, as well as characterizing their use, activity, and behavior. I propose a taxonomy of spam and false accounts, enumerating known techniques used to create and detect them. Then, I summarize studies estimating the prevalence of spam and false accounts on Twitter. Finally, I report on research that illustrates how spam and false accounts are used for scams and frauds, stock market manipulation, political disinformation and deception, conspiracy amplification, coordinated influence, public health misinformation campaigns, radical propaganda and recruitment, and more. I will conclude with a set of recommendations aimed at charting the path forward to combat these problems.

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arXiv2022

FairFed: Enabling Group Fairness in Federated Learning

Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment. Federated learning has been viewed as a promising solution for collaboratively training machine learning models among multiple parties while maintaining the privacy of their local data. However, federated learning also poses new challenges in mitigating the potential bias against certain populations (e.g., demographic groups), as this typically requires centralized access to the sensitive information (e.g., race, gender) of each datapoint. Motivated by the importance and challenges of group fairness in federated learning, in this work, we propose FairFed, a novel algorithm for fairness-aware aggregation to enhance group fairness in federated learning. Our proposed approach is server-side and agnostic to the applied local debiasing thus allowing for flexible use of different local debiasing methods across clients. We evaluate FairFed empirically versus common baselines for fair ML and federated learning, and demonstrate that it provides fairer models particularly under highly heterogeneous data distributions across clients. We also demonstrate the benefits of FairFed in scenarios involving naturally distributed real-life data collected from different geographical locations or departments within an organization.

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arXiv2022

From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection

The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media. Combating this coronavirus infodemic demands effective methods to detect fake news. To this end, we propose a method to infer news credibility from hashtags involved in news dissemination on social media, motivated by the tight connection between hashtags and news credibility observed in our empirical analyses. We first introduce a new graph that captures all (direct and \textit{indirect}) relationships among hashtags. Then, a language-independent semi-supervised algorithm is developed to predict fake news based on this constructed graph. This study first investigates the indirect relationship among hashtags; the proposed approach can be extended to any homogeneous graph to capture a comprehensive relationship among nodes. Language independence opens the proposed method to multilingual fake news detection. Experiments conducted on two real-world datasets demonstrate the effectiveness of our approach in identifying fake news, especially at an \textit{early} stage of propagation.

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