Persuasion Strategies

Modeling what makes an advertisement persuasive, i.e., eliciting the desired response from consumer, is critical to the study of propaganda, social psychology, and marketing. Despite its importance, computational modeling of persuasion in computer vision is still in its infancy, primarily due to the lack of benchmark datasets that can provide persuasion-strategy labels associated with ads. Motivated by persuasion literature in social psychology and marketing, we introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. The dataset also provides image segmentation masks, which labels persuasion strategies in the corresponding ad images on the test split.

The dataset contains dialogs of different LLMs from the discussion phase of a text-based Among Us-like game. The phrases in the dataset were annotated according to 25 selected persuasion techniques: Appeal to Logic, Appeal to Emotion, Appeal to Credibility, Shifting the Burden of Proof, Bandwagon Effect, Distraction, Gaslighting, Appeal to Urgency, Deception, Lying, Feigning Ignorance, Vagueness, Minimization, Self-Deprecation, Projection, Appeal to Relationship, Humor, Sarcasm, Withholding Information, Exaggeration, Denial without Evidence, Strategic Voting Suggestion, Appeal to Rules, Confirmation Bias Exploitation, Information Overload. The annotation was performed automatically by few-shot prompting a Gemini Flash 1.5 model with a temperature of 0. On a random sample of 11 games involving a total of 509 persuasion tags, Krippendorff's alpha inter-rater agreement between human annotations and the persuasion tagger was 0.56. For the definitions of the persuasion techniques, please refer to the associated publication: Among Them: A game-based framework for assessing persuasion capabilities of LLMs.

A novel corpus of 15,356 Polish web articles, including articles identified as containing disinformation. Our dataset enables a multifaceted understanding of disinformation. We present a distinctive multilayered methodology for annotating disinformation in texts. What sets our corpus apart is its focus on uncovering hidden intent and manipulation in disinformative content. A team of experts annotated each article with multiple labels indicating both disinformation creators’ intents and the manipulation techniques employed.

Werewolf Among Us is a dataset multimodal dataset for modeling persuasion behaviors. It contains 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. Source: Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games Image Source: https://arxiv.org/pdf/2212.08279v1.pdf