Social media platforms can alter algorithms to hinder the spread of misinformation and to create a distinction between how legitimate news sources and unreliable news sources appear in a person’s feed. This may also involve suggesting correct information that refutes misinformation that someone has viewed as suggested viewing
Problem: algorithms fail sometimes, they might not detect a fake news source. 

Solution to Misinformation: Algorithmic

Using fact checking to debunk misinformation.

Solution to Misinformation: Corrective

Using laws to regulate news and quell disinformation.

Solution to Misinformation: Legislative

- Don’t repeat wrong information because of the repition-based truth-effect.
- Institutions should work to increase people’s trust in them to reduce health-related anxiety.
- Active inoculating and prebunking.
- Repeated warnings.
- Improve people’s health literacy.


Pan, Liu and Fang's (2021) recommendations on how to fight the spread of health misinformation

Humor is often implemented by scientists when they present information. Humor is closely related to mirth, which refers to people's perceptions of how funny, humorous, and amusing a stimulus is. Interestingly, humor and mirth can have an effect on how scientific information is communicated. Therefore, they can likely be used as a method to combat misinformation.

Solution to Misinformation: Humor

Emotional refutations are statements that include emotional content and attempt to correct misinformation.

Solution to Misinformation: Emotional Refutations

Social Media platforms have started integrating mechanics that allow for the identification of fake news. For instance, such features have been introduced in Weibo, which is a Chinese social media platform.

Solutions to Misinformation: Social Media Disclaimers

Authors Jeff Gary and Ashkan Soltani argue that in order to reduce the spread of misinformation and harmful content on social media, rather than mandating specific content based regulations, it is more effective to address the business models under which social media platforms operate .

According to the authors, the underlying platform business models encourage and enable the spread of harmful content, including fake news. The business practice of most of these private company platforms is mainly based on tailored and targeted advertising. 

What enables targeted advertising is data collection from users. And in order to collect more data about the interests, preferences, personality, and behavior of specific users, social media platforms need to maximize the frequency and intensity with which users users interact with their platform.

Misinformation, harmful speeches, and conspiracy theories are financially advantageous to the platforms because they increase engagement of users and illicit responses. Fake news may be shared by users when it is is reaffirming of their sense of identity, community, and culture. When users spend more time on these platforms, more of their data can be collected and the more exposed they are to targeted advertising.

It is not just users that promote divisive and controversial contents; the platforms themselves promote such speech to garner user attention. Examples that the authors provide are Facebook's permission, until recently, to use the category "Jew haters" by advertisers for targeted advertisement and how Facebook distributed anti-vaccination ads to potential young mothers.

Addressing the Problem of Social Media Misinformation by Focusing on Social Media Companies' Business Practices

Researchers have been creating electronic simulations as means to train people to identify fake news. These are often presented in the form of games, and they simulate online platforms where misinformation and fake news are spread.

Electronic Simulation Interventions for Fake News

This research paper explains the potential psychology behind political misinformation, as well as how the issue type and source of the correction of misinformation may affect the effectiveness of the correction. They also include where the literature has not yet developed and how measurement methods may be involved. 

Political Misinformation Methods of Correction by Jennifer Jerit and Yangzi Zhao

Allowing users to view a list of guidelines for detecting a fake news post or article themselves. Works by priming critical thinking mechanisms. Could be a useful correction method on social media platforms like Facebook. 

Guideline Correction

A retraction of misinformation at the end of a news article significantly reduces reliance on misinformation

Official Retraction (print)

Many researchers believe that correcting the misinformation immediately on social media and news platforms is best way to stopping the spread of misinformation. A perfect example would be fact checking tweets made by verified users on twitter. However, this is being disproved to being an effective way to stopping the spread of misinformation.

Background Information: Negated Corrections and Misinformation

While most research on misinformation is aimed at adults, some research has also investigated how misinformation interventions can be used within an educational context to serve younger populations.

Solutions to Misinformation for Children

Warning-based approaches were found to be most well-liked by users and also most effective at preventing the spread of misinformation in a study investigating different types of misinformation interventions on social media.

Warning-based Approaches

- The Problem of Social Media Companies Business Practices with Misinformation
- How to solve the problem of social media companies encouraging misinformation

Social Media Companies and Misinformation

Also known as "conspiratorial ideation", this refers to the general tendency to endorse and believe in conspiracy theories. Those high in conspiracy ideations are less likely to believe in scientific findings and tend to form insular communities that restrict the exposure of correct information.

Conspiratorial Thinking

An accuracy prompt requires an individual to actively consider the concept of accuracy before evaluating a piece of information, such as a news headline. These prompts are a proactive intervention, helping prevent the spread of misinformation without relying on tech companies or government regulators to fact-check content. Research indicates that accuracy prompts can significantly reduce the spread of misinformation, with studies demonstrating up to a 71.7% increase in sharing discernment following their use.

Accuracy Prompts

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

Davidson College

University of Delaware

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

Misinformation 

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

van der Linden, S., & Roozenbeek, J. (2021). Psychological Inoculation Against Fake News. In R. Greifeneder, M. E. Jaffé, E. J. Newman, & N. Schwarz, The Psychology of Fake News: Accepting, Sharing, and Correcting Misinformation. Routledge. https://www.taylorfrancis.com/books/9780429295379

Psychological Inoculation Against Fake News

Yeo, S. K., Anderson, A. A., Becker, A. B., & Cacciatore, M. A. (2020). Scientists as comedians: The effects of humor on perceptions of scientists and scientific messages. Public Understanding of Science, 29(4), 408-418. https://doi.org/10.1177/0963662520915359

The effects of humor on perceptions of scientists and scientific messages.

Trevors, G., & Kendeou, P. (2020). The effects of positive and negative emotional text content on knowledge revision. Quarterly Journal of Experimental Psychology, 73(9), 1326-1339. https://doi.org/10.1177/1747021820913816

The effects of positive and negative emotional text content on knowledge revision

Ng, K. C., Tang, J., & Lee, D. (2021). The effect of platform intervention policies on fake news dissemination and survival: An empirical examination. Journal of Management Information Systems, 38(4), 898-930. https://doi.org/10.1080/07421222.2021.1990612

The Effect of Platform Intervention Policies on Fake News Dissemination and Survival: An Empirical Examination

- 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

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 

- 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

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

References for Exposure to social engagement metrics increases vulnerability to misinformation

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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