arXiv:2606. 26115v1 Announce Type: cross Abstract: This paper proposes a multi-layer AI framework for information landscape analysis in the context of information disorder.
arXiv:2208. 11582v2 Announce Type: replace-cross Abstract: The wide spread of false information online, including misinformation and disinformation, has become a major problem for our highly digitised and globalised society.
arXiv:2607. 20487v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent information settings where factual errors and ideological distortions are high-stakes.
The paper introduces a structured framework for claim-level discourse analysis in dense health narratives, addressing the limitations of existing topic- or sentiment-based representations. It identifies an average of 13.22 atomic claims per minute in social media health videos and proposes tuples that link each claim to thematic aspects, stance, and multidimensional pragmatic attributes. A benchmark of 1,191 manually annotated claims from 60 videos across four health domains is created, and experiments show that large language models perform well on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, indicating a need for task-specific inference strategies.
The paper presents the Temporal Coherence Score (TCS), a continuous, interpretable metric for detecting temporal inconsistencies in political news. TCS is computed through a four‑stage pipeline that extracts temporal facts, builds a temporal knowledge graph, verifies consistency using internal rules and external references, and aggregates scores with explanations. On a benchmark of 100 political articles with injected errors, TCS achieves 0.909 precision, providing detailed explanations for each flagged inconsistency.
FakeSpotter is a new tool that estimates the viral misinformation risk of textual content by measuring structural fingerprints of misinformation instead of directly judging truthfulness. It operates across linguistic, narrative, logical, and critical‑thinking dimensions, using repeated large language model assessments and domain‑specific logistic regression classifiers for both short and long texts. In a labeled corpus of 764 texts, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts, and its interpretive layer offers explainable outputs such as feature‑based scores, signal agreement, and a caution index for social listening.
arXiv:2606. 27736v1 Announce Type: new Abstract: The rapid spread of fake news poses increasing threats to information ecosystems, especially as AI-generated misinformation under Generative Engine Optimization (GEO) poisoning allows adversarially crafted content to be systematically surfaced by retrieval systems, contaminating LLM reasoning.
The paper introduces an unsupervised framework that identifies and characterizes competing narratives in political discourse on social media, specifically analyzing German politicians' tweets. It uses a multi‑stage pipeline incorporating topic modeling, event detection, and event linking to form coherent stories and reveal distinct user community perspectives. Two case studies on polarizing issues demonstrate the method’s effectiveness in uncovering divergent viewpoints and framing conflicts around trending political topics.
Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve jointly, yet they are typically studied in isolation.
arXiv:2405. 17838v3 Announce Type: replace-cross Abstract: Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media.
arXiv:2608.21389v1 Announce Type: cross Abstract: Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject st...
The paper introduces a three-tier evaluation framework—recovery, mining, and discovery—for unsupervised narrative label generation in disinformation datasets. It compares clustering-based and graph-community-based pipelines across seven datasets, finding that clustering can underrepresent prominent topics while graph methods produce many singletons that human annotators recognize as valid narratives. The authors release human-validated narrative candidate labels for the Climate Obstruction and PolyNarrative datasets to aid taxonomy development and dataset expansion.
arXiv:2606. 26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting and contradicting evidence coexist.