We introduce SPOT (Stopping Points in Online Threads), the first annotated corpus translating the sociological concept of stopping point into a reproducible NLP task. Stopping points are ordinary critical interventions that pause or redirect online discussions through a range of forms (irony, subtle doubt or fragmentary arguments) that frameworks like counterspeech or social correction often overlook. We operationalize this concept as a binary classification task and provide reliable annotation guidelines. The corpus contains 43,305 manually annotated French Facebook comments linked to URLs flagged as false information by social media users, enriched with contextual metadata (article, post, parent comment, page or group, and source). We benchmark fine-tuned encoder models (CamemBERT) and instruction-tuned LLMs under various prompting strategies. Results show that fine-tuned encoders outperform prompted LLMs in F1 score by more than 10 percentage points, confirming the importance of supervised learning for emerging non-English social media tasks. Incorporating contextual metadata further improves encoder models F1 scores from 0.75 to 0.78. We release the anonymized dataset, along with the annotation guidelines and code in our code repository, to foster transparency and reproducible research.

Detecting abusive language in social media conversations poses significant challenges, as identifying abusiveness often depends on the conversational context, characterized by the content and topology of preceding comments.Traditional Abusive Language Detection (ALD) models often overlook this context, which can lead to unreliable performance metrics.Recent Natural Language Processing (NLP) approaches that incorporate conversational context often rely on limited or overly simplified representations of this context, leading to inconsistent and sometimes inconclusive results.In this paper, we propose a novel approach that utilizes graph neural networks (GNNs) to model social media conversations as graphs, where nodes represent comments, and edges capture reply structures.We systematically investigate various graph representations and context windows to identify the optimal configurations for ALD.Our GNN model outperforms both context-agnostic baselines and linear context-aware methods, achieving significant improvements in F1 scores.These findings demonstrate the critical role of structured conversational context and establish GNNs as a robust framework for advancing context-aware ALD.Our code is available at this link.

To understand why internet users spread fake news online, many studies have focused on individual drivers, such as cognitive skills, media literacy, or demographics. Recent findings have also shown the role of complex socio-political dynamics, highlighting that political polarization and ideologies are closely linked to a propensity to participate in the dissemination of fake news. Most of the existing empirical studies have focused on the US example by exploiting the self-reported or solicited positioning of users on a dichotomous scale opposing liberals with conservatives. Yet, left-right polarization alone is insufficient to study socio-political dynamics when considering non binary and multi-dimensional party systems, in which relevant ideological stances must be characterized in additional dimensions, relating for example to opposition to elites, government, political parties or mainstream media. In this article we leverage ideological embeddings of Twitter networks in France in multi-dimensional opinions spaces, where dimensions stand for attitudes towards different issues, and we trace the positions of users who shared articles that were rated as misinformation by fact-checkers. In multi-dimensional settings, and in contrast with the US, opinion dimensions capturing attitudes towards elites are more predictive of whether a user shares misinformation. Most users sharing misinformation hold salient anti-elite sentiments and, among them, more so those with radical left- and right-leaning stances. Our results reinforce the importance of enriching one-dimensional left-right analyses, showing that other ideological dimensions, such as anti-elite sentiment, are critical when characterizing users who spread fake news. This lends support to emerging accounts of social drivers of misinformation through political polarization, but also stresses the role of the entanglement between fake news, anti-elite polarization, and the role of scientific authorities in public debate.

This paper presents a contribution to the study of bibliographic corpora through science mapping. From a graph representation of documents and their textual dimension, stochastic block models can provide a simultaneous clustering of documents and words that we call a domain-topic model. Previous work investigated the resulting topics, or word clusters, while ours focuses on the study of the document clusters we call domains. To enable the description and interactive navigation of domains, we introduce measures and interfaces that consider the structure of the model to relate both types of clusters. We then present a procedure that extends the block model to cluster metadata attributes of documents, which we call a domain-chained model, noting that our measures and interfaces transpose to metadata clusters. We provide an example application to a corpus relevant to current science, technology and society (STS) research and an interesting case for our approach: the abstracts presented between 1995 and 2017 at the American Society of Clinical Oncology Annual Meeting, the major oncology research conference. Through a sequence of domain-topic and domain-chained models, we identify and describe a group of domains that have notably grown through the last decades and which we relate to the establishment of "oncopolicy" as a major concern in oncology.

Traditionally, public opinion on different issues of public debate has been studied through polls and surveys. Recent advancements in network ideological scaling methods, however, have shown that digital behavioral traces in social media platforms can be used to mine opinions at a massive scale. This has yet to be shown to work beyond one-dimensional opinion scales, which are best suited for two-party systems and binary social divides such as those observed in the US. In this article, we use multidimensional ideological scaling for coupled with referential attitudinal data for some nodes. We show that opinions can be mined in a multitude of issues: from social networks, embedding them in ideological spaces where dimensions stand for indicators of positive and negative opinions, towards issues of public debate. This method does not require text analysis and is thus language independent. We illustrate this approach on the Twitter follower network of French users leveraging political survey data.

The prevalence of algorithmic recommendations has raised public concern about undesired societal effects. A central threat is the risk of polarization, which is difficult to conceptualize and to measure, making it difficult to assess the role of Recommender Systems in this phenomenon. These difficulties have yielded two types of analyses: 1) purely topological approaches that study how recommenders isolate or connect types of nodes in a graph, and 2) spatial opinion approaches that study how recommenders change the distribution of users on a given opinion scale. The former analyses prove inadequate in settings where users are not classified into categorical types (e.g., in two-party systems with binary social divides), while the latter rely on synthetic data due to the unobservability of opinions. To overcome both difficulties we present the first analysis of friend recommendations acting on real-world sub-graphs of the Twitter network where users are embedded in multidimensional ideological spaces and in which dimensions are indicators of attitudes towards issues in the public debate. We present a polarization metric adapted to these dual topological and spatial states of social network, and use it to track both the evolution of polarization on Twitter networks where the graph evolves following well-known Recommender Systems, and opinions co-evolve following a DeGroot opinion model. We show that different recommendation principles can sometimes drive or mitigate polarization appearing in real social networks.

A growing literature on ideology estimation through scaling methods in social networks restricts the scaling procedure to nodes that provide interpretability of the resulting feature space. On Twitter, for example, it is common to consider the subnetwork of parliamentarians and their followers. While effective in inferring meaningful ideological features, this restriction limits interesting applications such as country-wide measurement of polarization and its evolution. We propose two methods to propagate ideological features beyond these sub-networks: based on homophily (linked users have similar ideology), and based on structural similarity (nodes with similar neighborhoods have similar ideologies). In our methods, we leverage the concept of ideological coherence of a neighborhood as a parameter for propagation. Using Twitter data, we produce an ideological scaling for 370K users, and analyze the two propagation methods on a population of 6.5M users. We find that, when coherence is considered, the ideology of a user is better estimated from those with similar neighborhoods, than from their immediate neighbors.