As narratives shape public opinion and influence societal actions, distinguishing between truthful and misleading narratives has become a significant challenge. To address this, we introduce the EU DisinfoTest, a novel benchmark designed to evaluate the efficacy of Language Models in identifying disinformation narratives. Developed through a Human-in-the-Loop methodology and grounded in research from EU DisinfoLab, the EU DisinfoTest comprises more than 1,300 narratives. Our benchmark includes persuasive elements under Logos, Pathos, and Ethos rhetorical dimensions. We assessed state-of-the-art LLMs, including the newly released GPT-4o, on their capability to perform zero-shot classification of disinformation narratives versus credible narratives. Our findings reveal that LLMs tend to regard narratives with authoritative appeals as trustworthy, while those with emotional appeals are frequently incorrectly classified as disinformative. These findings highlight the challenges LLMs face in nuanced content interpretation and suggest the need for tailored adjustments in LLM training to better handle diverse narrative structures.

A growing body of research exploits social media behaviors to gauge psychological character-istics, though trait empathy has received little attention. Because of its intimate link to the abil-ity to relate to others, our research aims to predict participants’ levels of empathy, given their textual and friending behaviors on Facebook. Using Poisson regression, we compared the vari-ance explained in Davis’ Interpersonal Reactivity Index (IRI) scores on four constructs (em-pathic concern, personal distress, fantasy, perspective taking), by two classes of variables: 1) post content and 2) linguistic style. Our study lays the groundwork for a greater understanding of empathy’s role in facilitating interactions on social media.

News articles about the same event published over time have properties that challenge NLP and IR applications. A cluster of such texts typically exhibits instances of paraphrase and contradiction, as sources update the facts surrounding the story, often due to an ongoing investigation. The current hypothesis is that the stories “evolve” over time, beginning with the first text published on a given topic. This is tested using a phylogenetic approach as well as one based on language modeling. The fit of the evolutionary models is evaluated with respect to how well they facilitate the recovery of chronological relationships between the documents. Over all data clusters, the language modeling approach consistently outperforms the phylogenetics model. However, on manually collected clusters in which the documents are published within short time spans of one another, both have a similar performance, and produce statistically significant results on the document chronology recovery evaluation.

Multi-document summaries produced via sentence extraction often suffer from a number of cohesion problems, including dangling anaphora, sudden shifts in topic and incorrect or awkward chronological ordering. Therefore, the development of an automated revision process to correct such problems is a research area of current interest. We present the RevisionBank, a corpus of 240 extractive, multi-document summaries that have been manually revised to promote cohesion. The summaries were revised by six linguistic students using a constrained set of revision operations that we previously developed. In the current paper, we describe the process of developing a taxonomy of cohesion problems and corrective revision operators that address such problems, as well as an annotation schema for our corpus. Finally, we discuss how our taxonomy and corpus can be used for the study of revision-based multi-document summarization as well as for summary evaluation.

Clusters of multiple news stories related to the same topic exhibit a number of interesting properties. For example, when documents have been published at various points in time or by different authors or news agencies, one finds many instances of paraphrasing, information overlap and even contradiction. The current paper presents the Cross-document Structure Theory (CST) Bank, a collection of multi-document clusters in which pairs of sentences from different documents have been annotated for cross-document structure theory relationships. We will describe how we built the corpus, including our method for reducing the number of sentence pairs to be annotated by our hired judges, using lexical similarity measures. Finally, we will describe how CST and the CST Bank can be applied to different research areas such as multi-document summarization.