Misinformation warnings: Twitter’s soft moderation effects on COVID-19 vaccine belief echoes

Sharevski, Filipo; Alsaadi, Raniem; Jachim, Peter; Pieroni, Emma

doi:10.1016/j.cose.2021.102577

Cited by 55 publications

(36 citation statements)

References 32 publications

(51 reference statements)

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“…Due to the negative potential influence on people’s health practices, health misinformation has received more scholarly attention, especially since the beginning of the COVID-19 pandemic [ 80 – 82 ]. It is particularly harmful because: 1) people are more likely to trust the information after they have been exposed to it, 2) correcting misinformation is time-consuming and resource-intensive, and 3) even after correction, it may continue to influence attitudes and behaviours, reflecting a phenomenon known as “belief echoes” [ 83 , 84 ]. Correcting disinformation has become more complex and difficult as social media platforms have grown in popularity, catalysing the quick and widespread spread of misinformation.…”

Section: Discussionmentioning

confidence: 99%

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Abdelkader

Lokker

2022

BMC Complement Med Ther

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Background Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the paucity of evidence, various complementary, alternative and integrative medicines (CAIMs) have been being touted as both preventative and curative. We conducted sentiment and emotion analysis with the intent of understanding CAIM content related to COVID-19 being generated on Twitter across 9 months. Methods Tweets relating to CAIM and COVID-19 were extracted from the George Washington University Libraries Dataverse Coronavirus tweets dataset from March 03 to November 30, 2020. We trained and tested a machine learning classifier using a large, pre-labelled Twitter dataset, which was applied to predict the sentiment of each CAIM-related tweet, and we used a natural language processing package to identify the emotions based on the words contained in the tweets. Results Our dataset included 28 713 English-language Tweets. The number of CAIM-related tweets during the study period peaked in May 2020, then dropped off sharply over the subsequent three months; the fewest CAIM-related tweets were collected during August 2020 and remained low for the remainder of the collection period. Most tweets (n = 15 612, 54%) were classified as positive, 31% were neutral (n = 8803) and 15% were classified as negative (n = 4298). The most frequent emotions expressed across tweets were trust, followed by fear, while surprise and disgust were the least frequent. Though volume of tweets decreased over the 9 months of the study, the expressed sentiments and emotions remained constant. Conclusion The results of this sentiment analysis enabled us to establish key CAIMs being discussed at the intersection of COVID-19 across a 9-month period on Twitter. Overall, the majority of our subset of tweets were positive, as were the emotions associated with the words found within them. This may be interpreted as public support for CAIM, however, further qualitative investigation is warranted. Such future directions may be used to combat misinformation and improve public health strategies surrounding the use of social media information.

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Section: Discussionmentioning

confidence: 99%

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Abdelkader

Lokker

2022

BMC Complement Med Ther

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“…Evidence suggest that only the interstitial covers, but not the trustworthiness tags, make the users heed the warnings of misinformation [64,65,69,84]. It is tempting to simply discard the trustworthiness tags and only use interstitial covers, however.…”

Section: Achtung! Misinformationmentioning

confidence: 99%

“…What actually is a bit difficult to understand is why, despite these advancements in usable security, warnings about misinformation on social media have made little progress in fostering desirable security behavior [69]. One could argue that the nature of the security hazard differs between the two settings -traditional programmatic security is far more complex to grasp than picking up on a causal post that links the COVID-19 vaccines with infertility -and that makes designing misinformation warnings an entirely different challenge.…”

Section: Introductionmentioning

confidence: 99%

Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

Sharevski¹,

Devine²,

Pieroni³

et al. 2022

Preprint

Self Cite

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Warning users about misinformation on social media is not a simple usability task. Soft moderation has to balance between debunking falsehoods and avoiding moderation bias while preserving the social media consumption flow. Platforms thus employ minimally distinguishable warning tags with generic text under a suspected misinformation content. This approach resulted in an unfavorable outcome where the warnings "backfired" and users believed the misinformation more, not less. In response, we developed enhancements to the misinformation warnings where users are advised on the context of the information hazard and exposed to standard warning iconography. We ran an A/B evaluation with the Twitter's original warning tags in a 337 participant usability study. The majority of the participants preferred the enhancements as a nudge toward recognizing and avoiding misinformation. The enhanced warning tags were most favored by the politically left-leaning and to a lesser degree moderate participants, but they also appealed to roughly a third of the right-leaning participants. The education level was the only demographic factor shaping participants' preferences. We use our findings to propose user-tailored improvements in the soft moderation of misinformation on social media. CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; Usability in security and privacy.

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“…Researchers in a preliminary study found that some means of soft moderation on social media sites like Twitter are more promising than others, including warnings which fully obscure content like Twitter's "Read First" warning [45]. Levying soft moderation techniques like these may encourage users to read the full context of news content, as well as serve as a reminder of the dangers of out-of-context content in perpetuating the spread of alt-narratives online.…”

Section: Guarding Against Adversarial Language Modelingmentioning

confidence: 99%

“…Another option would be to automate even more of the process. While an adversary might extend a couple of modules that we built for the Out-of-Context Summarizer summaries to post on social media platforms without any human intervention, with ethical considerations, we could develop automatically-generated simulated tweets or parleys for experimentation to gauge user reactions in a lab setting, similar to user studies investigating the effects of soft moderation on Twitter [45].…”

Section: Future Enhancementsmentioning

confidence: 99%

"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

Jachim¹,

Sharevski²,

Pieroni³

2021

Preprint

Self Cite

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This paper presents Out-of-Context Summarizer, a tool that takes arbitrary public news articles out of context by summarizing them to coherently fit either a liberal-or conservative-leaning agenda. The Out-of-Context Summarizer also suggests hashtag keywords to bolster the polarization of the summary, in case one is inclined to take it to Twitter, Parler or other platforms for trolling. Out-of-Context Summarizer achieved 79% precision and 99% recall when summarizing COVID-19 articles, 93% precision and 93% recall when summarizing politically-centered articles, and 87% precision and 88% recall when taking liberally-biased articles out of context. Summarizing valid sources instead of synthesizing fake text, the Outof-Context Summarizer could fairly pass the "adversarial disclosure" test, but we didn't take this easy route in our paper. Instead, we used the Out-of-Context Summarizer to push the debate of potential misuse of automated text generation beyond the boilerplate text of responsible disclosure of adversarial language models. CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; • Computing methodologies → Information extraction;

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Misinformation warnings: Twitter’s soft moderation effects on COVID-19 vaccine belief echoes

Cited by 55 publications

References 32 publications

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

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