scholarly journals Who Posted That Story? Processing Layered Sources in Facebook News Posts

2019 ◽  
Vol 97 (1) ◽  
pp. 141-160 ◽  
Author(s):  
Anne Oeldorf-Hirsch ◽  
Christina L. DeVoss
Keyword(s):  
Social Media ◽  
Information Processing ◽  
Mediation Model ◽  
Cognitive Elaboration ◽  
Cognitive Mediation ◽  
News Sources ◽  
Media Source ◽  
Heuristic Systematic Model ◽  
Social Media Platforms ◽  
Source Cues

With social media platforms becoming primary news sources, concerns about credibility judgments and knowledge grow. This study ( N = 233) experimentally tests the effects of multiple source cues on Facebook news posts on credibility and knowledge. Judgments of story credibility were directly influenced by media source cues, but not friend source cues. Involvement in the source topic moderated the effects of these source cues, such that particular combinations influenced credibility differently, and also influenced cognitive elaboration about the topic. Theoretical implications for cognitive mediation model of learning from the news and the heuristic-systematic model of information processing are presented.

scholarly journals Conceptualizing different forms of news processing following incidental news contact: A triple-path model

Journalism ◽  
2020 ◽  
Vol 21 (8) ◽  
pp. 1049-1066 ◽  
Author(s):  
Mareike Wieland ◽  
Katharina Kleinen-von Königslöw
Keyword(s):  
Social Media ◽  
Information Processing ◽  
Dual System ◽  
Path Model ◽  
Future Research ◽  
Active Engagement ◽  
Mediation Model ◽  
Cognitive Mediation ◽  
Starting Point ◽  
Empirical Approaches

Research on incidental news exposure in the context of social media focuses on ‘successful’ incidental news exposure – when unintended news contacts result in active engagement and knowledge gains. However, we lack both theoretical and empirical approaches to the far more likely case that people keep on scrolling through their newsfeed without any post triggering active engagement. To fill this gap, the article conceptualizes a triple-path model of incidental news exposure on social media as a process. Building upon the Cognitive Mediation Model, dual system theories on information processing and recent empirical findings, three different pathways of incidental news processing are identified: automatic, incidental and active. The triple-path model thus allows to theorize the learning potentials that can plausibly be expected from each incidental news exposure path as a starting point for future research.

Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks

2021 ◽  
pp. 194016122110091
Author(s):  
Magdalena Wojcieszak ◽  
Ericka Menchen-Trevino ◽  
Joao F. F. Goncalves ◽  
Brian Weeks
Keyword(s):  
Social Media ◽  
Search Engines ◽  
Direct Access ◽  
Data Set ◽  
Individual Level ◽  
News Sources ◽  
Internet Users ◽  
News Websites ◽  
The Us ◽  
Social Media Platforms

The online environment dramatically expands the number of ways people can encounter news but there remain questions of whether these abundant opportunities facilitate news exposure diversity. This project examines key questions regarding how internet users arrive at news and what kinds of news they encounter. We account for a multiplicity of avenues to news online, some of which have never been analyzed: (1) direct access to news websites, (2) social networks, (3) news aggregators, (4) search engines, (5) webmail, and (6) hyperlinks in news. We examine the extent to which each avenue promotes news exposure and also exposes users to news sources that are left leaning, right leaning, and centrist. When combined with information on individual political leanings, we show the extent of dissimilar, centrist, or congenial exposure resulting from each avenue. We rely on web browsing history records from 636 social media users in the US paired with survey self-reports, a unique data set that allows us to examine both aggregate and individual-level exposure. Visits to news websites account for about 2 percent of the total number of visits to URLs and are unevenly distributed among users. The most widespread ways of accessing news are search engines and social media platforms (and hyperlinks within news sites once people arrive at news). The two former avenues also increase dissimilar news exposure, compared to accessing news directly, yet direct news access drives the highest proportion of centrist exposure.

scholarly journals Distortions of political bias in crowdsourced misinformation flagging

2020 ◽  
Vol 17 (167) ◽  
pp. 20200020
Author(s):  
Michele Coscia ◽  
Luca Rossi
Keyword(s):  
Social Media ◽  
News Item ◽  
Real World Data ◽  
Media Environment ◽  
Political Bias ◽  
News Sources ◽  
Online Social Media ◽  
News Source ◽  
Social Media Platforms ◽  
Current Content

Many people view news on social media, yet the production of news items online has come under fire because of the common spreading of misinformation. Social media platforms police their content in various ways. Primarily they rely on crowdsourced ‘flags’: users signal to the platform that a specific news item might be misleading and, if they raise enough of them, the item will be fact-checked. However, real-world data show that the most flagged news sources are also the most popular and—supposedly—reliable ones. In this paper, we show that this phenomenon can be explained by the unreasonable assumptions that current content policing strategies make about how the online social media environment is shaped. The most realistic assumption is that confirmation bias will prevent a user from flagging a news item if they share the same political bias as the news source producing it. We show, via agent-based simulations, that a model reproducing our current understanding of the social media environment will necessarily result in the most neutral and accurate sources receiving most flags.

scholarly journals Comparison of Traditional Citation Metrics and Altmetrics Among Dermatology Journals: Content and Correlational Analysis Study (Preprint)

2019 ◽  
Author(s):  
Gregg Murray ◽  
Rebecca Hellen ◽  
James Ralph ◽  
Siona Ni Raghallaigh
Keyword(s):  
Social Media ◽  
Citation Count ◽  
Research Impact ◽  
Online News ◽  
Positive Correlation ◽  
News Sources ◽  
Journal Citation Reports ◽  
Social Media Platforms ◽  
The Impact ◽  
Article Citation

BACKGROUND Research impact has traditionally been measured using citation count and impact factor (IF). Academics have long relied heavily on this form of metric system to measure a publication’s impact. A higher number of citations is viewed as an indicator of the importance of the research and a marker for the impact of the publishing journal. Recently, social media and online news sources have become important avenues for dissemination of research, resulting in the emergence of an alternative metric system known as altmetrics. OBJECTIVE We assessed the correlation between altmetric attention score (AAS) and traditional scientific impact markers, namely journal IF and article citation count, for all the dermatology journal and published articles of 2017. METHODS We identified dermatology journals and their associated IFs available in 2017 using InCites Journal Citation Reports. We entered all 64 official dermatology journals into Altmetric Explorer, a Web-based platform that enables users to browse and report on all attention data for every piece of scholarly content for which Altmetric Explorer has found attention. RESULTS For the 64 dermatology journals, there was a moderate positive correlation between journal IF and journal AAS (<i>r<sub>s</sub></i>=.513, <i>P</i>&lt;.001). In 2017, 6323 articles were published in the 64 dermatology journals. Our data show that there was a weak positive correlation between the traditional article citation count and AAS (<i>r<sub>s</sub></i>=.257, <i>P</i>&lt;.001). CONCLUSIONS Our data show a weak correlation between article citation count and AAS. Temporal factors may explain this weak association. Newer articles may receive increased online attention after publication, while it may take longer for scientific citation counts to accumulate. Stories that are at times deemed newsworthy and then disseminated across the media and social media platforms border on sensationalism and may not be truly academic in nature. The opposite can also be true.

scholarly journals Will the crowd game the algorithm? Using layperson judgments to combat misinformation on social media by downranking distrusted sources

2019 ◽  
Author(s):  
Ziv Epstein ◽  
Gordon Pennycook ◽  
David Gertler Rand
Keyword(s):  
Social Media ◽  
Political Polarization ◽  
Survey Experiment ◽  
Fake News ◽  
News Sources ◽  
Ranking Algorithms ◽  
Social Media Platforms ◽  
Highly Correlated ◽  
High Level ◽  
Survey Responses

How can social media platforms fight the spread of misinformation? One possibility is to use newsfeed algorithms to downrank content from sources that users rate as untrustworthy. But will laypeople unable to identify misinformation sites due to motivated reasoning or lack of expertise? And will they “game” this crowdsourcing mechanism to promote content that aligns with their partisan agendas? We conducted a survey experiment in which N = 984 Americans indicated their trust in numerous news sites. Half of the participants were told that their survey responses would inform social media ranking algorithms - creating a potential incentive to misrepresent their beliefs. Participants trusted mainstream sources much more than hyper-partisan or fake news sources, and their ratings were highly correlated with professional fact-checker judgments. Critically, informing participants that their responses would influence ranking algorithms did not diminish this high level of discernment, despite slightly increasing the political polarization of trust ratings.

Fake News Finds an Audience

2019 ◽  
pp. 201-222
Author(s):  
Erik P. Bucy ◽  
John E. Newhagen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Information Processing ◽  
Educational Strategy ◽  
Fake News ◽  
False Information ◽  
User Experiences ◽  
The Social ◽  
Social Media Platforms ◽  
Media Systems

The vulnerabilities shown by media systems and individual users exposed to attacks on truth from fake news and computational propaganda in recent years should be considered in light of the characteristics and concerns surrounding big data, especially the volume and velocity of messages delivered over social media platforms that tax the average user’s capacity to determine their truth value in real time. For reasons explained by the psychology of information processing, a high percentage of fake news that reaches audiences is accepted as true, particularly when distractions and interruptions typify user experiences with technology. As explained in this essay, fake news thrives in environments lacking editorial policing and epistemological vigilance, making the social media milieu ideally suited for spreading false information. In response, we suggest the value of an educational strategy to combat the dilemma that digital disinformation poses to informed citizenship.

scholarly journals A Biopsychosocial Perspective of User-Generated Innovation in Open Innovation Models: A Moderated-Mediation Analysis

2021 ◽  
Vol 7 (2) ◽  
pp. 131
Author(s):  
Tahseen Arshi ◽  
Venkoba Rao ◽  
Kamal Qazi ◽  
Vazeerjan Begum ◽  
Mansoor ALSabahi ◽  
...  
Keyword(s):  
Social Media ◽  
Open Innovation ◽  
Moderated Mediation ◽  
Control Group ◽  
Exploratory Research ◽  
Mediation Model ◽  
Moderated Mediation Model ◽  
Proxy Measure ◽  
Social Media Platforms ◽  
Self Reporting Questionnaire

User-generated innovation has contributed to the growth of the democratization of open-innovation models. One of the most common forms of user-generated innovation is evident on social media platforms. The purpose of this study is to investigate nonpecuniary motivations that drive innovation among user innovators on social media platforms. Furthermore, the study examines the underlying sociopsychological and biological dispositions that influence nonpecuniary motivation. The experimental and control group consisted of 204 user innovators on different social media platforms who filled out a self-reporting questionnaire in this exploratory research design. The study assessed endocrinal biomarkers through a proxy measure of 2D:4D ratio associated with behavioral, emotional, and social behavior. It developed a moderated-mediation model evaluating the indirect conditional relationships through a regression-based analysis with bootstrapped estimations. The findings support the moderated-mediation model, indicating that nonpecuniary motivation primarily explains user innovator behavior. Hedonic emotions, characterized by aesthetics, experiential enjoyment, and satisfaction-related feelings, mediate this relationship. A critical finding of the study is that endocrinal testosterone moderates this mediated relationship. This study is the first to apply a biopsychosocial lens to examine motivational drives influencing user-generated innovation using a moderated-mediation model. It contributes to understanding user innovators’ tricky motivational purposes, emphasizing the role of human agency in advancing the open-innovation agenda.

Just a Glance, or More? Pathways from Counter-Attitudinal Incidental Exposure to Attitude (De)Polarization Through Response Behaviors and Cognitive Elaboration

2021 ◽  
Author(s):  
Hsuan-Ting Chen ◽  
Yonghwan Kim ◽  
Michael Chan
Keyword(s):  
Social Media ◽  
Middle Level ◽  
Active Engagement ◽  
Panel Survey ◽  
Mediation Model ◽  
Cognitive Elaboration ◽  
Attitude Polarization ◽  
Perceived Utility ◽  
Incidental Exposure ◽  
Serial Mediation

Abstract Using two-wave U.S. panel survey data, this study proposes a moderated serial mediation model to examine through what paths and under what conditions incidental exposure to counter-attitudinal information on social media would enhance or mitigate polarization. The findings suggest that such exposure can indirectly polarize attitude by eliciting passive scanning behaviors, but it can also indirectly attenuate attitude polarization first through active engagement with the counter-attitudinal information, then through cognitively elaborating on the information. However, the indirect depolarizing effect of incidental exposure to counter-attitudinal information on citizens’ attitude depends on the extent to which they are instrumentally motivated. The indirect effect occurs when an individual’s perceived utility of counter-attitudinal information is at a high and a middle level, but not at a low level. Implications of the findings are discussed.

Political news on Facebook during the 2019 Dutch elections

2020 ◽  
Author(s):  
Stijn Peeters ◽  
Richard Rogers
Keyword(s):  
Social Media ◽  
Academic Work ◽  
Election Campaigns ◽  
Political News ◽  
News Sources ◽  
Social Media Platforms ◽  
Specific Alternative ◽  
Media Monitoring ◽  
Monitoring Service

This chapter discusses Facebook-based engagement with news sources during the campaigns for two Dutch election campaigns in 2019. Building on earlier journalistic and academic work, a broad typology of ‘junk’ versus mainstream news is developed, as well as a number of more specific alternative categories. Engagement with news articles within these categories on Facebook is then analysed with BuzzSumo (a media monitoring service built atop CrowdTangle). While mainstream news receives significantly more engagement than other types of news during both campaigns, junk news also receives consistent and significant engagement, though no substantial engagement with outright disinformation is found. We conclude with a cursory comparison of the findings with those for other social media platforms, positioning Facebook as the platform where engagement with junk news is most significant.

scholarly journals Neutral bots probe political bias on social media

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wen Chen ◽  
Diogo Pacheco ◽  
Kai-Cheng Yang ◽  
Filippo Menczer
Keyword(s):  
Social Media ◽  
The Political ◽  
User Interactions ◽  
Political Bias ◽  
News Sources ◽  
Political Center ◽  
Social Media Platforms ◽  
Twitter Users ◽  
The Right ◽  
To Receive

AbstractSocial media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal accounts are exposed to moderate content shifting their experience toward the political center. Partisan accounts, especially conservative ones, tend to receive more followers and follow more automated accounts. Conservative accounts also find themselves in denser communities and are exposed to more low-credibility content.

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