Veracity Computing from Lexical Cues and Perceived Certainty Trends
We present a data-driven method for determining the veracity of a set of rumorous claims on social media data. Tweets from different sources pertaining to a rumor are processed on three levels: first, factuality values are assigned to each tweet based on four textual cue categories relevant for our journalism use case; these amalgamate speaker support in terms of polarity and commitment in terms of certainty and speculation. Next, the proportions of these lexical cues are utilized as predictors for tweet certainty in a generalized linear regression model. Subsequently, lexical cue proportions, predicted certainty, as well as their time course characteristics are used to compute veracity for each rumor in terms of the identity of the rumor-resolving tweet and its binary resolution value judgment. The system operates without access to extralinguistic resources. Evaluated on the data portion for which hand-labeled examples were available, it achieves .74 F1-score on identifying rumor resolving tweets and .76 F1-score on predicting if a rumor is resolved as true or false.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
It is commonly perceived that fake news and real news exhibit distinct writing styles, such as the use of sensationalist versus objective language. However, we emphasize that style-related features can also be exploited …
ArticlesFact CheckingFake News DetectionAcoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies
Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of de…
Deception DetectionDetecting Uncertainty Cues in Hungarian Social Media Texts
In this paper, we aim at identifying uncertainty cues in Hungarian social media texts. We present our machine learning based uncertainty detector which is based on a rich features set including lexical, morphological, sy…
BIG-bench Machine LearningDomain AdaptationInstance SearchIITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation
This paper describes our system participation in the SemEval-2017 Task 8 {`}RumourEval: Determining rumour veracity and support for rumours{'}. The objective of this task was to predict the stance and veracity of the und…
Decision MakingGeneral ClassificationRumour DetectionStruggle Premium : How Human Effort and Imperfection Drive Perceived Value in the Age of AI
As AI enters creative practice, audiences face growing uncertainty in judging authenticity and value. This study examines the Struggle Premium, the added value attributed to perceived human effort, by analyzing how visib…