Negativity Spreads More than Positivity on Twitter after both Positive and Negative Political Situations

Schöne, Jonas; Parkinson, Brian; Goldenberg, Amit

doi:10.31234/osf.io/x9e7u

Cited by 17 publications

(25 citation statements)

References 29 publications

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“…This is in agreement with previous research that found a bias toward negative sentiments in recall in transmission chain experiments (Bebbington et al, 2017), or in acceptance of information (Fessler et al, 2014). A negativity bias is consistent with a broad evolutionary logic that would advantage negative information in salience and memorability (Baumeister et al, 2001), and it has been suggested that could influence as well the diffusion of information online (Acerbi, 2019b;Bellovary et al, 2021;Melumad et al, 2021;Schöne et al, 2021). On the other side, the present experiments compared a negative narrative with a neutral one, without considering explicitly a positive condition.…”

Section: Discussionsupporting

confidence: 91%

From storytelling to Facebook. Content biases when retelling or sharing a story

Acerbi¹

2021

Preprint

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Cultural evolution researchers use transmission chain experiments to investigate which content is more likely to survive when transmitted from one individual to another. These experiments resemble oral storytelling, where individuals need to understand, memorise, and reproduce the content. However, prominent contemporary forms of cultural transmission—think an online sharing— only involve the willingness to transmit the content. Here I present two fully preregistered online experiments that explicitly investigated the differences between these two modalities of transmission. The first experiment (N=1080) examined whether negative content, information eliciting disgust, and threat-related information were better transmitted than their neutral counterpart in a traditional transmission chain set-up. The second experiment (N=1200), used the same material, but participants were asked whether they would share or not the content in two conditions: in a large anonymous social network, or with their friends, in their favourite social network. Negative content was both better transmitted in transmission chain experiments and shared more than its neutral counterpart. Threat-related information was successful in transmission chain experiments but not when sharing, and, finally, information eliciting disgust was not advantaged in either. Overall, the results present a composite picture, suggesting that the interactions between the specific content and the medium of transmission are important and, possibly, that content biases are stronger when memorisation and reproduction are involved in the transmission—like in oral transmission—than when they are not—like in online sharing.

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Section: Discussionsupporting

confidence: 91%

From storytelling to Facebook. Content biases when retelling or sharing a story

Acerbi¹

2021

Preprint

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“…Recent work has examined affect's role in driving online engagement in both positive and negative contexts (Schöne, Parkinson, & Goldenberg, 2021). This research indicates that in response to both positive and negative political events, negativity leads to greater engagement on Twitter.…”

Section: Expression and Engagement With Affective Content On Social Mediamentioning

confidence: 98%

Left- and Right-Leaning News Organizations' Negative Tweets are More Likely to be Shared

Bellovary¹,

Na²,

Goldenberg³

2021

Preprint

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Negativity has historically dominated news content; however, little research has examined how news organizations use affect on social media, where content is generally positive. In the current project we ask a few questions: Do news organizations on Twitter use negative or positive language and which type of affect garners more engagement on social media? Does the political orientation of new organizations impact the affect expressed and engagement tweets receive on social media? The goal of this project is to examine these questions by investigating tweets of 24 left- and 20 right-leaning news organizations (140,358 tweets). Results indicated that negative affect was expressed more than positive affect. Additionally, negativity predicted engagement with news organizations’ tweets, but positivity did not. Finally, there were no differences in affect between left- and right-leaning political orientations. Overall, it appears that for news organizations, negativity is more frequent and more impactful than positivity.

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“…A main outcome of the analysis was that negative content was preponderant in online misinformation, about five times more common than positive one. The advantage of negative content has been also detected in social media, with an analysis of a large dataset from Twitter, showing that negative tweets were more likely to be retweeted after political events, both negative and positive (Schöne et al, 2021). Similar results were found considering tweets from news organisations: negative affect was expressed more than positive, and predicted more engagement (Bellovary et al, 2021).…”

Section: The Spread Of Information Onlinementioning

confidence: 60%

Digital culture

Acerbi¹

2021

Preprint

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Cultural evolution can provide a useful framework to understand how information is produced, transmitted, and selected in contemporary online, digital, media. The diffusion of digital technologies triggered a radical departure from previous modalities of cultural transmission but, at the same time, general characteristics of human cultural evolution and cognition influence these developments. In this chapter, I will explore some areas where the links between cultural evolution research and digital media seem more promising. As cultural evolution-inspired research on internet phenomena is still in its infancy, these areas represent suggestions and links with works in other disciplines more than reviews of past research in cultural evolution. These include topics such as how to characterise the online effects of social influence and the spread of information; the possibility that digital, online, media could enhance cumulative culture; and the differences between online and offline cultural transmission. In the last section I will consider other possible future directions: the influences of different affordances in different media supporting cultural transmission; the role of producers of cultural traits; and, finally, some considerations on the effects on cultural dynamics of algorithms selecting information.

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Negativity Spreads More than Positivity on Twitter after both Positive and Negative Political Situations

Cited by 17 publications

References 29 publications

From storytelling to Facebook. Content biases when retelling or sharing a story

From storytelling to Facebook. Content biases when retelling or sharing a story

Left- and Right-Leaning News Organizations' Negative Tweets are More Likely to be Shared

Digital culture

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