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O’Riordan, Sheila; Kiely, Gaye; Emerson, Bill; Feller, Joseph

doi:10.1145/3306446.3340818

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Birdwatch has been launched by Twitter on January 23, 2021, and aims to identify misleading social media posts by harnessing the wisdom of crowds. Different from earlier small-scale crowd-based initiatives to fact-checking [6,10,16,35], Birdwatch allows users to identify misleading tweets directly on Twitter and write short (max 280 characters) fact-checks (so-called "Birdwatch notes") that add context to the tweet. Another unique feature of Birdwatch is that authors of Birdwatch notes additionally need to answer checkbox questions when identifying misleading posts.…”

Section: Data and Methodology 31 Data Collectionmentioning

confidence: 99%

“…Birdwatch: Informed by experimental studies, the social media platform Twitter has recently launched its community-based fact-checking system Birdwatch [40,52]. Different from earlier crowd-based fact-checking initiatives [6,10,16,35], Birdwatch allows users to identify misinformation directly on the platform (see next section for details). Given the recency of the platform, research on Birdwatch is scant.…”

Section: Introductionmentioning

confidence: 99%

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Believability and Harmfulness Shape the Virality of Misleading Social Media Posts

Drolsbach,

Pröllochs

2023

Preprint

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Misinformation on social media presents a major threat to modern societies. While previous research has analyzed the virality across true and false social media posts, not every misleading post is necessarily equally viral. Rather, misinformation has different characteristics and varies in terms of its believability and harmfulness -which might influence its spread. In this work, we study how the perceived believability and harmfulness of misleading posts are associated with their virality on social media. Specifically, we analyze (and validate) a large sample of crowd-annotated social media posts from Twitter's Birdwatch platform, on which users can rate the believability and harmfulness of misleading tweets. To address our research questions, we implement an explanatory regression model and link the crowd ratings for believability and harmfulness to the virality of misleading posts on Twitter. Our findings imply that misinformation that is (i) easily believable and (ii) not particularly harmful is associated with more viral resharing cascades. These results offer insights into how different kinds of crowd fact-checked misinformation spreads and suggest that the most viral misleading posts are often not the ones that are particularly concerning from the perspective of public safety. From a practical view, our findings may help platforms to develop more effective strategies to curb the proliferation of misleading posts on social media. CCS CONCEPTS• Human-centered computing → Social media; Empirical studies in collaborative and social computing; • Applied computing → Sociology.

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Section: Data and Methodology 31 Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Believability and Harmfulness Shape the Virality of Misleading Social Media Posts

Drolsbach,

Pröllochs

2023

Preprint

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Investigating Differences in Crowdsourced News Credibility Assessment

Bhuiyan

Zhang

Sehat³

et al. 2020

Proc. ACM Hum.-Comput. Interact.

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Misinformation about critical issues such as climate change and vaccine safety is oftentimes amplified on online social and search platforms. The crowdsourcing of content credibility assessment by laypeople has been proposed as one strategy to combat misinformation by attempting to replicate the assessments of experts at scale. In this work, we investigate news credibility assessments by crowds versus experts to understand when and how ratings between them differ. We gather a dataset of over 4,000 credibility assessments taken from 2 crowd groups---journalism students and Upwork workers---as well as 2 expert groups---journalists and scientists---on a varied set of 50 news articles related to climate science, a topic with widespread disconnect between public opinion and expert consensus. Examining the ratings, we find differences in performance due to the makeup of the crowd, such as rater demographics and political leaning, as well as the scope of the tasks that the crowd is assigned to rate, such as the genre of the article and partisanship of the publication. Finally, we find differences between expert assessments due to differing expert criteria that journalism versus science experts use---differences that may contribute to crowd discrepancies, but that also suggest a way to reduce the gap by designing crowd tasks tailored to specific expert criteria. From these findings, we outline future research directions to better design crowd processes that are tailored to specific crowds and types of content.

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Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media

Jahanbakhsh

Zhang

Berinsky

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges---1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate---on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response.

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Do you have a source for that?

Cited by 5 publications

References 35 publications

Believability and Harmfulness Shape the Virality of Misleading Social Media Posts

Believability and Harmfulness Shape the Virality of Misleading Social Media Posts

Investigating Differences in Crowdsourced News Credibility Assessment

Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media

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