Online information is a critical societal threat in the digital age, and social media platforms are used a major vehicle to spread it. Misinformation can cause serious societal harm, in multiple ways: affecting public health, influencing public policy, instigating violence. spreading conspiracies. reducing trust in authorities, and increase polarization and conflict. The growing societal impact of misinformation has driven research on technical solutions to detect and stop actors that generate and spread such content. There is currently little empirical research on the effects of interface elements of social media feeds on the spread of misinformation. To address this gap, they empirically investigated how the spread of low-credibility content is affected by expo-sure to typical social engagement metrics, i.e., the numbers of Likes and Shares shown for a news article.

Implications of the study

What effect does exposure to social engagement metrics have on people's propensity to share content?

Does exposure to high numbers for social engagement metrics increase the chances that people will like and share questionable content and/or make it less likely that people will engage in fact checking of low-credibility sources?

The Research Questions

They investigated the effect of social engagement metrics on the spread of information from low-credibility sources using Fakey, a new literacy game that stimulates a social media feed. The game presents players with actual current news articles from mainstream and low-credibility media sources. A randomly generated social engagement metric is displayed with each presented article. Then the players are instructed to share, like, fact check, or skip articles. Over 19-months after deploying the game, they analyzed game-play sessions of over 8,500 unique players, mainly from the United States, involving approximately 120,000 articles, half from sources flagged as low-credibility by news and fact-checking organizations. 

The Experimental Design

Their findings suggest that the display of social engagement metrics can strongly influence interaction with low-credibility information. The higher the engagement, the more prone people are to sharing questionable content and less to fact checking it. These findings imply that social media platforms must rethink how engagement metrics should be displayed such that they do not facilitate the spread of misinformation or hinder the spread of legitimate information. Further research is needed to guard against malicious tampering with engagement metrics at an early stage and to design education interventions that teach users to prioritize trustworthiness of news courses over engagement metrics.

The Findings

Indiana University

Information that is (verifiably) misleading, false, or incomplete. Misinformation can be created with or without the intent to misinform the public. 

Misinformation 

Avram, M., Micallef, N., Patil, S., & Menczer, F. (2020). Exposure to social engagement metrics increases vulnerability to misinformation. Harvard Kennedy School (HKS) Misinformation Review. https://doi.org/10.37016/mr-2020-033

Exposure to social engagement metrics increases vulnerability to misinformation

- The information appears to be true or is believable 
- Being rewritten in different forms
- Being accessible through multiple sources
- Confirmation Bias 
- Authoritative bias 
- Targeting hot topics
- Indicating urgency
- The Sunk Cost Fallacy 
- emotional appeals 
- False Beliefs
- Labels can lead to errors in assessment of information
- False Memories
- Inaccurate evaluation of information
- Ease of Processing
- Underthinking
- Deliberative processes

Reasons Misinformation Spreads

Rumors, conspiracy theories, satire news, and hoaxes

Examples of Misinformation

The process of determining the accuracy of information. Often performed and published by professional fact checkers such as Politifact or Snopes.

Fact Checking

- algorithmic
- corrective
- legislative
- psychological
- humor
- emotional refutations
- social media disclaimers

Possible solutions to Misinformation

Forewarning against misinformation should not only refute the incorrect information, it should also explain the dangers of misinformation in general. This is more effective than refuting alone. Further explored in Eakin, Schreiber, & Sergent-Marshall, 2003.

Effective forewarning against misinformation

- Because there is no single source of data that covers all three modes of consuming news, researchers select data sources on "the basis of their availability," which doesn't necessarily correspond with representativeness.
- The studies done on fake news often fail to consider how much of the fake news is consumed relative to other types of news or non-news related content.
- Even when the prevalence of fake news is low compared to other content, fake news can still be considered important because of its disproportionate impact on certain populations. Therefore comprehensive questions of prevalence on their own cannot resolve questions about influence.

Reasons Why It Is Difficult to Empirically Evaluate Disagreements Over the Prevalence and Importance of Misinformation

According to Guess & Lyons (2020) misinformation and disinformation differ regarding the author's/creater's intent. Misinformation and Disinformation are often difficult to distinguish because it’s difficult to tell whether someone spread wrong information intentionally (Disinformation) or unintentionally (Misinformation). If political misinformation is spread by politicians it's usually considered as disinformation. Also, fake news which are organized and spread by established news organizations are considered disinformation.

Distinction between Misinformation and Disinformation

Oftentimes, the producers of misinformation hide their identity. However, a few producers have been identified, e.g. Macedonian teenagers and Russia’s Internet Research Agency (IRA).

Production of Misinformation

It's difficult to trace misinformation back to their original source. Sources of fake news tend to be hyperpartisan and far-right (pro-Trump during the 2016 U.S. election). This notion is supported that even in mainstream, left-wing journalists tend to follow journalistic guidelines and codes more than right-wing journalists. In the past years, automated bots have much contributed to the spread of misinformation. It has been found than certain social media sites such as Facebook are better for spreading fake news than others such as twitter. 

Supply of Misinformation

Guess et al. (2018): approximately 27% of Americans had consumed at least one fake news article over the five weeks before the U.S. presidential election in 2016. Fourney et al. (2016): ca. 0.34% of Microsoft Explorer users (who agreed to anonymously share their internet usage) visited a fake news domain in a day. Grinberg et al. (2019): on twitter, ca. 5% of political content originates from fake news sources in 2016. Guess et al. (2018): 0.01 % of tweets contained fake news during the French presidential election in 2017. 

Even thought these statistics have their limitations and are difficult to interpret, one can say that only a small amount of social media content is fake news and only a small amount of social media users is responsible for the huge dissemination of fake news.

Prevalence of Misinformation

Scientific racism is the use of pseudoscience to claim inherent racial superiority of some groups over others. It fails to account for social and political factors when explaining racial inequalities and justifies them by using the authority of science as an objective way of knowledge formation (Gould, 1996).

Scientific Racism 

This concept refers to the attempt to correct the spread of misinformation by reducing people’s misconceptions. This topic focuses on ways to offer misinformation correction, the number of corrections offered to make an impact, and the source of the misinformation corrections. 

Correcting Misinformation

- Health Misinformation
- Political Misinformation
- Fake News
- Disinformation
- Misinformation in Online Communities

Types of Misinformation

The extent to which a person believes misinformation relative to accurate content

Truth Discernment

Topic of research project on visualization of information and misinformation.

Identifying Misinformation through Visualization

References for Who falls for fake news? Psychological and clinical profiling evidence of fake news consumers

Misleading information that is presented to someone after witnessing event can change how they recall and explain that event. One common explanation is retroactive interference (new learning interfering with old memory/understanding)

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