arXiv subjects

Giovanni Luca Ciampaglia

Publications and source records attributed to Giovanni Luca Ciampaglia.

At least 19 recordsLinked to original sources

arXiv2026

Challenging Partisan Expectations Reduces Political Polarization

Political conversations are often proposed as a remedy for political polarization, yet their effectiveness remains inconsistent. We argue that this inconsistency partly reflects a neglected feature of political contact: the expectations partisans bring to these encounters. We hypothesize that conversations should reduce political polarization the most when they violate the expected link between partisan identity and issue position. We test this hypothesis in a 2x2 experiment in which 1,983 U.S. adults engaged in structured conversations with an AI chatbot whose presented partisan identity and policy stance were independently manipulated. We find that expectation-challenging conversations in which participants talk with a disagreeing ingroup member or an agreeing outgroup member are effective in reducing affective and issue polarization. Although these effects emerge without meaningful shifts in participants' own policy positions, a follow-up survey shows that most effects disappear over one month. Interestingly, these conversations maintain or improve objective measures of deliberation but are experienced as less satisfying by participants. Our findings identify expectation violation as an underexplored depolarization mechanism. Our results also demonstrate the promises and limitations of how conversational AI can serve as a scalable method for experimentally studying interventions to mitigating partisan divides.

cs.SI↗

arXiv2026

Platform architecture determines whether recommendation algorithms can shape information quality on social media

Social media platforms shape public discourse through two fundamental design choices that naturally co-occur in any field investigation: platform architecture, which defines what types of actors exist and how they interact, and recommendation algorithm, which determines what content is surfaced to users. Using agent-based simulation, we orthogonally manipulate both factors, exploring four prototypical architectures -- tree (e.g., Reddit), layered hierarchy (e.g., Facebook), network (e.g., Twitter), and complete graph (e.g., TikTok) -- and two algorithms: chronological (LIFO) and popularity-based (Hot). Drawing on prior theory that identifies and ranks canonical system architectures in terms of their flexibility we hypothesize that algorithmic effects on information spread and quality should be largest on the most flexible platforms and smallest on the most constrained ones. We find strong confirmation of this prediction. On tree-like platforms like Reddit, the algorithm has no detectable effect on information spread and quality. On layered hierarchies and networks like Facebook and Twitter, respectively, the Hot algorithm has modest positive effects on both the spread of information and its quality. On complete structures like TikTok, the Hot algorithm leads to a winner-take-all dynamics that has strong negative effects on both information spread and quality, making the relation between content quality and popularity unpredictable. These findings imply that architectural considerations are more powerful levers than algorithmic interventions for the design of healthy online spaces and public discourse. Platform reform efforts focused exclusively on algorithm choice may be insufficient on architecturally unconstrained platforms and unnecessary on architecturally constrained ones.

cs.SI↗

arXiv2026

Understanding the Gap Between Stated and Revealed Preferences in News Curation: A Study of Young Adult Social Media Users

Social media feed algorithms infer user preferences from their past behaviors. Yet what drives engagement often diverges from what users value. We examine this gap between stated preferences (what users say they prefer) and revealed preferences (what their behavior suggests they prefer) among young adults, a group deeply embedded in algorithmically mediated environments. Using a mixed-methods approach combining surveys and interviews with feed curation activities, we investigate: what gaps exist between stated and revealed preferences; how users make sense of these gaps; what values users believe should guide algorithmic curation; and how systems might reflect those values. Participants often found themselves engaging with low-quality content they did not endorse, despite wanting high-quality information. When asked to curate an ideal social media news feed for a hypothetical persona, participants created feeds they considered more satisfying and higher in quality by prioritizing values such as accuracy and diversity. In doing so, they navigated trade-offs between different values, factoring in social relationships and context surrounding the persona. These findings suggest that feed curation is a socially situated process of judging what should be visible and appropriate in shared information spaces. Based on these insights, we offer design directions for bridging the gap between stated and revealed preferences.

cs.HC↗

arXiv2025

MurkySky: Analyzing News Reliability on Bluesky

Bluesky has recently emerged as a lively competitor to Twitter/X for a platform for public discourse and news sharing. Most of the research on Bluesky so far has focused on characterizing its adoption due to migration. There has been less interest on characterizing the properties of Bluesky as a platform for news sharing and discussion, and in particular the prevalence of unreliable information on it. To fill this gap, this research provides the first comprehensive analysis of news reliability on Bluesky. We introduce MurkySky, a public tool to track the prevalence of content from unreliable news sources on Bluesky. Using firehose data from the summer of 2024, we find that on Bluesky reliable-source news content is prevalent, and largely originating from left-leaning sources. Content from unreliable news sources, while accounting for a small fraction of all news-linking posts, tends to originate from more partisan sources, but largely reflects the left-leaning skew of the platform. Analysis of the language and hashtags used in news-linking posts shows that unreliable-source content concentrates on specific topics of discussion.

cs.SI↗

arXiv2024

Survival of the Notable: Gender Asymmetry in Wikipedia Collective Deliberations

Communities on the web rely on open conversation forums for a number of tasks, including governance, information sharing, and decision making. However these forms of collective deliberation can often result in biased outcomes. A prime example are Articles for Deletion (AfD) discussions on Wikipedia, which allow editors to gauge the notability of existing articles, and that, as prior work has suggested, may play a role in perpetuating the notorious gender gap of Wikipedia. Prior attempts to address this question have been hampered by access to narrow observation windows, reliance on limited subsets of both biographies and editorial outcomes, and by potential confounding factors. To address these limitations, here we adopt a competing risk survival framework to fully situate biographical AfD discussions within the full editorial cycle of Wikipedia content. We find that biographies of women are nominated for deletion faster than those of men, despite editors taking longer to reach a consensus for deletion of women, even after controlling for the size of the discussion. Furthermore, we find that AfDs about historical figures show a strong tendency to result into the redirecting or merging of the biography under discussion into other encyclopedic entries, and that there is a striking gender asymmetry: biographies of women are redirected or merged into biographies of men more often than the other way round. Our study provides a more complete picture of the role of AfD in the gender gap of Wikipedia, with implications for the governance of the open knowledge infrastructure of the web.

cs.HC↗

arXiv2023

Factuality Challenges in the Era of Large Language Models

The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incredibly useful, natural-sounding tools mark significant advances in natural language generation, yet they exhibit a propensity to generate false, erroneous, or misleading content -- commonly referred to as "hallucinations." Moreover, LLMs can be exploited for malicious applications, such as generating false but credible-sounding content and profiles at scale. This poses a significant challenge to society in terms of the potential deception of users and the increasing dissemination of inaccurate information. In light of these risks, we explore the kinds of technological innovations, regulatory reforms, and AI literacy initiatives needed from fact-checkers, news organizations, and the broader research and policy communities. By identifying the risks, the imminent threats, and some viable solutions, we seek to shed light on navigating various aspects of veracity in the era of generative AI.

cs.CL↗

arXiv2023

The role of online attention in the supply of disinformation in Wikipedia

Wikipedia and many User-Generated Content (UGC) communities are known for producing reliable, quality content, but also for being vulnerable to false or misleading information. Previous work has shown that many hoaxes on Wikipedia go undetected for extended periods of time. But little is known about the creation of intentionally false or misleading information online. Does collective attention toward a topic increase the likelihood it will spawn disinformation? Here, we measure the relationship between allocation of attention and the production of hoax articles on the English Wikipedia. Analysis of traffic logs reveals that, compared to legitimate articles created on the same day, hoaxes tend to be more associated with traffic spikes preceding their creation. This is consistent with the idea that the supply of false or misleading information on a topic is driven by the attention it receives. These findings improve our comprehension of the determinants of disinformation in UGC communities and could help promote the integrity of knowledge on Wikipedia.

cs.CY↗

arXiv2021

REMOD: Relation Extraction for Modeling Online Discourse

The enormous amount of discourse taking place online poses challenges to the functioning of a civil and informed public sphere. Efforts to standardize online discourse data, such as ClaimReview, are making available a wealth of new data about potentially inaccurate claims, reviewed by third-party fact-checkers. These data could help shed light on the nature of online discourse, the role of political elites in amplifying it, and its implications for the integrity of the online information ecosystem. Unfortunately, the semi-structured nature of much of this data presents significant challenges when it comes to modeling and reasoning about online discourse. A key challenge is relation extraction, which is the task of determining the semantic relationships between named entities in a claim. Here we develop a novel supervised learning method for relation extraction that combines graph embedding techniques with path traversal on semantic dependency graphs. Our approach is based on the intuitive observation that knowledge of the entities along the path between the subject and object of a triple (e.g. Washington,_D.C.}, and United_States_of_America) provides useful information that can be leveraged for extracting its semantic relation (i.e. capitalOf). As an example of a potential application of this technique for modeling online discourse, we show that our method can be integrated into a pipeline to reason about potential misinformation claims.

cs.SI↗

arXiv2021

Political audience diversity and news reliability in algorithmic ranking

Newsfeed algorithms frequently amplify misinformation and other low-quality content. How can social media platforms more effectively promote reliable information? Existing approaches are difficult to scale and vulnerable to manipulation. In this paper, we propose using the political diversity of a website's audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards. We then incorporate audience diversity into a standard collaborative filtering framework and show that our improved algorithm increases the trustworthiness of websites suggested to users -- especially those who most frequently consume misinformation -- while keeping recommendations relevant. These findings suggest that partisan audience diversity is a valuable signal of higher journalistic standards that should be incorporated into algorithmic ranking decisions.

cs.SI↗

arXiv2020

HONEM: Learning Embedding for Higher Order Networks

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher-order dependencies in the network. Thus, the embeddings that are generated may not accurately represent of the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this paper presents HONEM, a higher-order network embedding method that captures the non-Markovian higher-order dependencies in a network. HONEM is specifically designed for the higher-order network structure (HON) and outperforms other state-of-the-art methods in node classification, network re-construction, link prediction, and visualization for networks that contain non-Markovian higher-order dependencies.

cs.LG↗

arXiv2020

Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers

While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as "echo chambers." Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.

cs.CY↗

arXiv2018

The spread of low-credibility content by social bots

The massive spread of digital misinformation has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of misinformation online and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. With few exceptions, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots played a disproportionate role in amplifying low-credibility content. Accounts that actively spread articles from low-credibility sources are significantly more likely to be bots. Automated accounts are particularly active in amplifying content in the very early spreading moments, before an article goes viral. Bots also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, retweeting bots who post links to low-credibility content. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.

cs.SI↗

arXiv2018

Anatomy of an online misinformation network

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.

cs.SI↗

arXiv2018

Network segregation in a model of misinformation and fact checking

Misinformation under the form of rumor, hoaxes, and conspiracy theories spreads on social media at alarming rates. One hypothesis is that, since social media are shaped by homophily, belief in misinformation may be more likely to thrive on those social circles that are segregated from the rest of the network. One possible antidote is fact checking which, in some cases, is known to stop rumors from spreading further. However, fact checking may also backfire and reinforce the belief in a hoax. Here we take into account the combination of network segregation, finite memory and attention, and fact-checking efforts. We consider a compartmental model of two interacting epidemic processes over a network that is segregated between gullible and skeptic users. Extensive simulation and mean-field analysis show that a more segregated network facilitates the spread of a hoax only at low forgetting rates, but has no effect when agents forget at faster rates. This finding may inform the development of mitigation techniques and overall inform on the risks of uncontrolled misinformation online.

cs.SI↗

arXiv2017

RelSifter: Scoring Triples from Type-like Relations - The Samphire Triple Scorer at WSDM Cup 2017

We present RelSifter, a supervised learning approach to the problem of assigning relevance scores to triples expressing type-like relations such as 'profession' and 'nationality.' To provide additional contextual information about individuals and relations we supplement the data provided as part of the WSDM 2017 Triple Score contest with Wikidata and DBpedia, two large-scale knowledge graphs (KG). Our hypothesis is that any type relation, i.e., a specific profession like 'actor' or 'scientist,' can be described by the set of typical "activities" of people known to have that type relation. For example, actors are known to star in movies, and scientists are known for their academic affiliations. In a KG, this information is to be found on a properly defined subset of the second-degree neighbors of the type relation. This form of local information can be used as part of a learning algorithm to predict relevance scores for new, unseen triples. When scoring 'profession' and 'nationality' triples our experiments based on this approach result in an accuracy equal to 73% and 78%, respectively. These performance metrics are roughly equivalent or only slightly below the state of the art prior to the present contest. This suggests that our approach can be effective for evaluating facts, despite the skewness in the number of facts per individual mined from KGs.

cs.IR↗

arXiv2017

Finding Streams in Knowledge Graphs to Support Fact Checking

The volume and velocity of information that gets generated online limits current journalistic practices to fact-check claims at the same rate. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. To this end, we present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a (subject, predicate, object) triple. We view a knowledge graph of background information about real-world entities as a flow network, and knowledge as a fluid, abstract commodity. We show that computational fact checking of such a triple then amounts to finding a "knowledge stream" that emanates from the subject node and flows toward the object node through paths connecting them. Evaluation on a range of real-world and hand-crafted datasets of facts related to entertainment, business, sports, geography and more reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful path patterns and surface relevant facts that may help a human fact checker corroborate or refute a claim.

cs.AI↗

arXiv2017

How algorithmic popularity bias hinders or promotes quality

Algorithms that favor popular items are used to help us select among many choices, from engaging articles on a social media news feed to songs and books that others have purchased, and from top-raked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, beautiful movies, prestigious information sources, and important discoveries --- in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and ultimately lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content "bubble up" in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the critical trade-off between quality and popularity. We find a regime of intermediate exploration cost where an optimal balance exists, such that choosing what is popular actually promotes high-quality items to the top. Outside of these limits, however, popularity bias is more likely to hinder quality. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

cs.CY↗

arXiv2016

Gendered Conversation in a Social Game-Streaming Platform

Online social media and games are increasingly replacing offline social activities. Social media is now an indispensable mode of communication; online gaming is not only a genuine social activity but also a popular spectator sport. With support for anonymity and larger audiences, online interaction shrinks social and geographical barriers. Despite such benefits, social disparities such as gender inequality persist in online social media. In particular, online gaming communities have been criticized for persistent gender disparities and objectification. As gaming evolves into a social platform, persistence of gender disparity is a pressing question. Yet, there are few large-scale, systematic studies of gender inequality and objectification in social gaming platforms. Here we analyze more than one billion chat messages from Twitch, a social game-streaming platform, to study how the gender of streamers is associated with the nature of conversation. Using a combination of computational text analysis methods, we show that gendered conversation and objectification is prevalent in chats. Female streamers receive significantly more objectifying comments while male streamers receive more game-related comments. This difference is more pronounced for popular streamers. There also exists a large number of users who post only on female or male streams. Employing a neural vector-space embedding (paragraph vector) method, we analyze gendered chat messages and create prediction models that (i) identify the gender of streamers based on messages posted in the channel and (ii) identify the gender a viewer prefers to watch based on their chat messages. Our findings suggest that disparities in social game-streaming platforms is a nuanced phenomenon that involves the gender of streamers as well as those who produce gendered and game-related conversation.

cs.SI↗