Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines

Gabriel, Saadia; Hallinan, Skyler; Sap, Maarten; Nguyen, Pemi; Roesner, Franziska; Choi, Eunsol; Choi, Yejin

doi:10.18653/v1/2022.acl-long.222

Cited by 13 publications

(16 citation statements)

References 51 publications

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“…Most similar to our work is the work by Gabriel et al [27] that creates "perceived" news frames from headlines regarding how people think about misinformation. They argued that an effective way to predict how news headlines will influence readers requires understanding how readers perceive the intent behind the news.…”

Section: Semantic Analysis and Understanding The Newsmentioning

confidence: 58%

“…In this work, we also explore implicit items within the text, e.g., estimating implicit sentiment towards the cyclist mentioned in the headline from the writer's perspective. Our framework builds upon the work of [24,27,67] by integrating their key insights into a hierarchical framework for analyzing the pragmatic meaning of news headlines. Our framework will allow us to assess the ways in which news organizations structure their coverage of events or subjects and to evaluate the impact of this coverage on public attitudes and perceptions.…”

Section: Semantic Analysis and Understanding The Newsmentioning

confidence: 99%

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Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News

Xingmeng¹,

Walton²,

Shrestha³

et al. 2023

Preprint

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Increasing the number of cyclists, whether for general transport or recreation, can provide health improvements and reduce the environmental impact of vehicular transportation. However, the public's perception of cycling may be driven by the ideologies and reporting standards of news agencies. For instance, people may identify cyclists on the road as "dangerous" if news agencies overly report cycling accidents, limiting the number of people that cycle for transportation. Moreover, if fewer people cycle, there may be less funding from the government to invest in safe infrastructure. In this paper, we explore the perceived perception of cyclists within news headlines. To accomplish this, we introduce a new dataset, "Bike Frames", that can help provide insight into how headlines portray cyclists and help detect accident-related headlines. Next, we introduce a multi-task (MT) regularization approach that increases the detection accuracy of accident-related posts, demonstrating improvements over traditional MT frameworks. Finally, we compare and contrast the perceptions of cyclists with motorcyclist-related headlines to ground the findings with another related activity for both male-and female-related posts. Our findings show that general news websites are more likely to report accidents about cyclists than other events. Moreover, cyclist-specific websites are more likely to report about accidents than motorcycling-specific websites, even though there is more potential danger for motorcyclists. Finally, we show substantial differences in the reporting about male vs. female-related persons, e.g., more male-related cyclists headlines are related to accidents, but more female-related motorcycling headlines about accidents.WARNING: This paper contains descriptions of accidents and death. 1 CCS Concepts: • Computing methodologies → Discourse, dialogue and pragmatics; Natural language processing; • Human-centered computing → Collaborative and social computing.

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Section: Semantic Analysis and Understanding The Newsmentioning

confidence: 58%

Section: Semantic Analysis and Understanding The Newsmentioning

confidence: 99%

Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News

Xingmeng¹,

Walton²,

Shrestha³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Tan et al [14] analysed the effect of the content of a tweet on its reach. More recent studies like Gabriel et al [8] demonstrated how the reach of news articles can be estimated using models based on GPT-2 [10] and T5 [11]. However, their approaches provide only a coarsegrained analysis and do not significantly contribute to performing a comparative analysis.…”

Section: Related Workmentioning

confidence: 99%

“…The first heuristic baseline fine-tunes FLANG-RoBERTa [12] to make direct predictions. To evaluate the feasibility of deploying zero-shot LLMs for the proposed task, we let FLAN-UL2 [15,18], Claude, and React T5 [8] make predictions. Going one step further, we solicit various LLMs' analysis of 𝑡1 and 𝑡2 to determine which LLM can guide the classification model to deliver enhanced performance.…”

Section: Experiments 51 Experimental Settingsmentioning

confidence: 99%

Generator-Guided Crowd Reaction Assessment

Ghosh,

Chen,

Naskar

2024

Companion Proceedings of the ACM Web Conference 2024

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In the realm of social media, understanding and predicting post reach is a significant challenge. This paper presents a Crowd Reaction AssessMent (CReAM) task designed to estimate if a given social media post will receive more reaction than another, a particularly essential task for digital marketers and content writers. We introduce the Crowd Reaction Estimation Dataset (CRED), consisting of pairs of tweets from The White House with comparative measures of retweet count. The proposed Generator-Guided Estimation Approach (GGEA) leverages generative Large Language Models (LLMs), such as ChatGPT, FLAN-UL2, and Claude, to guide classification models for making better predictions. Our results reveal that a fine-tuned FLANG-RoBERTa model, utilizing a cross-encoder architecture with tweet content and responses generated by Claude, performs optimally. We further use a T5-based paraphraser to generate paraphrases of a given post and demonstrate GGEA's ability to predict which post will elicit the most reactions. We believe this novel application of LLMs provides a significant advancement in predicting social media post reach.

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“…Pennycook et al (2020) show that people tend to believe false claims about COVID-19 and share false information when they do not think critically about the accuracy and veracity of the information. Recent work aims at understanding reader perception of news reliability (Gabriel et al 2022). Kouzy et al (2020) show that ∼ 25% of COVID-19 messages contain some form of misinformation and ∼ 17% contain some unverifiable information.…”

Section: Related Workmentioning

confidence: 99%

Drink Bleach or Do What Now? COVID-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation

Dharawat

Lourentzou

Morales

et al. 2022

ICWSM

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Given the widespread dissemination of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the research community. Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts. In this work, we frame health misinformation as a risk assessment task. More specifically, we study the severity of each misinformation story and how readers perceive this severity, i.e., how harmful a message believed by the audience can be and what type of signals can be used to recognize potentially malicious fake news and detect refuted claims. To address our research questions, we introduce a new benchmark dataset, accompanied with detailed data analysis. We evaluate several traditional and state-of-the-art models, and show there is a significant gap in performance when applying traditional misinformation classification models to this task. We conclude with open challenges and future directions.

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Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines

Cited by 13 publications

References 51 publications

Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News

Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News

Generator-Guided Crowd Reaction Assessment

Drink Bleach or Do What Now? COVID-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation

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