essay on social media and fake news

June 21, 2018

Biases Make People Vulnerable to Misinformation Spread by Social Media

Researchers have developed tools to study the cognitive, societal and algorithmic biases that help fake news spread

By Giovanni Luca Ciampaglia , Filippo Menczer & The Conversation US

Roy Scott Getty Images

The following essay is reprinted with permission from The Conversation , an online publication covering the latest research.

Social media are among the  primary sources of news in the U.S.  and across the world. Yet users are exposed to content of questionable accuracy, including  conspiracy theories ,  clickbait ,  hyperpartisan content ,  pseudo science  and even  fabricated “fake news” reports .

It’s not surprising that there’s so much disinformation published: Spam and online fraud  are lucrative for criminals , and government and political propaganda yield  both partisan and financial benefits . But the fact that  low-credibility content spreads so quickly and easily  suggests that people and the algorithms behind social media platforms are vulnerable to manipulation.

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Explaining the tools developed at the Observatory on Social Media.

Our research has identified three types of bias that make the social media ecosystem vulnerable to both intentional and accidental misinformation. That is why our  Observatory on Social Media  at Indiana University is building  tools  to help people become aware of these biases and protect themselves from outside influences designed to exploit them.

Bias in the brain

Cognitive biases originate in the way the brain processes the information that every person encounters every day. The brain can deal with only a finite amount of information, and too many incoming stimuli can cause  information overload . That in itself has serious implications for the quality of information on social media. We have found that steep competition for users’ limited attention means that  some ideas go viral despite their low quality —even when people prefer to share high-quality content.*

To avoid getting overwhelmed, the brain uses a  number of tricks . These methods are usually effective, but may also  become biases  when applied in the wrong contexts.

One cognitive shortcut happens when a person is deciding whether to share a story that appears on their social media feed. People are  very affected by the emotional connotations of a headline , even though that’s not a good indicator of an article’s accuracy. Much more important is  who wrote the piece .

To counter this bias, and help people pay more attention to the source of a claim before sharing it, we developed  Fakey , a mobile news literacy game (free on  Android  and  iOS ) simulating a typical social media news feed, with a mix of news articles from mainstream and low-credibility sources. Players get more points for sharing news from reliable sources and flagging suspicious content for fact-checking. In the process, they learn to recognize signals of source credibility, such as hyperpartisan claims and emotionally charged headlines.

Bias in society

Another source of bias comes from society. When people connect directly with their peers, the social biases that guide their selection of friends come to influence the information they see.

In fact, in our research we have found that it is possible to  determine the political leanings of a Twitter user  by simply looking at the partisan preferences of their friends. Our analysis of the structure of these  partisan communication networks  found social networks are particularly efficient at disseminating information – accurate or not – when  they are closely tied together and disconnected from other parts of society .

The tendency to evaluate information more favorably if it comes from within their own social circles creates “ echo chambers ” that are ripe for manipulation, either consciously or unintentionally. This helps explain why so many online conversations devolve into  “us versus them” confrontations .

To study how the structure of online social networks makes users vulnerable to disinformation, we built  Hoaxy , a system that tracks and visualizes the spread of content from low-credibility sources, and how it competes with fact-checking content. Our analysis of the data collected by Hoaxy during the 2016 U.S. presidential elections shows that Twitter accounts that shared misinformation were  almost completely cut off from the corrections made by the fact-checkers.

When we drilled down on the misinformation-spreading accounts, we found a very dense core group of accounts retweeting each other almost exclusively – including several bots. The only times that fact-checking organizations were ever quoted or mentioned by the users in the misinformed group were when questioning their legitimacy or claiming the opposite of what they wrote.

Bias in the machine

The third group of biases arises directly from the algorithms used to determine what people see online. Both social media platforms and search engines employ them. These personalization technologies are designed to select only the most engaging and relevant content for each individual user. But in doing so, it may end up reinforcing the cognitive and social biases of users, thus making them even more vulnerable to manipulation.

For instance, the detailed  advertising tools built into many social media platforms  let disinformation campaigners exploit  confirmation bias  by  tailoring messages  to people who are already inclined to believe them.

Also, if a user often clicks on Facebook links from a particular news source, Facebook will  tend to show that person more of that site’s content . This so-called “ filter bubble ” effect may isolate people from diverse perspectives, strengthening confirmation bias.

Our own research shows that social media platforms expose users to a less diverse set of sources than do non-social media sites like Wikipedia. Because this is at the level of a whole platform, not of a single user, we call this the  homogeneity bias .

Another important ingredient of social media is information that is trending on the platform, according to what is getting the most clicks. We call this  popularity bias , because we have found that an algorithm designed to promote popular content may negatively affect the overall quality of information on the platform. This also feeds into existing cognitive bias, reinforcing what appears to be popular irrespective of its quality.

All these algorithmic biases can be manipulated by  social bots , computer programs that interact with humans through social media accounts. Most social bots, like Twitter’s  Big Ben , are harmless. However, some conceal their real nature and are used for malicious intents, such as  boosting disinformation  or falsely  creating the appearance of a grassroots movement , also called “astroturfing.” We found  evidence of this type of manipulation  in the run-up to the 2010 U.S. midterm election.

To study these manipulation strategies, we developed a tool to detect social bots called  Botometer . Botometer uses machine learning to detect bot accounts, by inspecting thousands of different features of Twitter accounts, like the times of its posts, how often it tweets, and the accounts it follows and retweets. It is not perfect, but it has revealed that as many as  15 percent of Twitter accounts show signs of being bots .

Using Botometer in conjunction with Hoaxy, we analyzed the core of the misinformation network during the 2016 U.S. presidential campaign. We found many bots exploiting both the cognitive, confirmation and popularity biases of their victims and Twitter’s algorithmic biases.

These bots are able to construct filter bubbles around vulnerable users, feeding them false claims and misinformation. First, they can attract the attention of human users who support a particular candidate by tweeting that candidate’s hashtags or by mentioning and retweeting the person. Then the bots can amplify false claims smearing opponents by retweeting articles from low-credibility sources that match certain keywords. This activity also makes the algorithm highlight for other users false stories that are being shared widely.

Understanding complex vulnerabilities

Even as our research, and others’, shows how individuals, institutions and even entire societies can be manipulated on social media, there are  many questions  left to answer. It’s especially important to discover how these different biases interact with each other, potentially creating more complex vulnerabilities.

Tools like ours offer internet users more information about disinformation, and therefore some degree of protection from its harms. The solutions will  not likely be only technological , though there will probably be some technical aspects to them. But they must take into account  the cognitive and social aspects  of the problem.

*Editor’s note: This article was updated on Jan. 10, 2019, to remove a link to a study that has been retracted. The text of the article is still accurate, and remains unchanged.

This article was originally published on The Conversation . Read the original article .

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The impact of fake news on social media and its influence on health during the COVID-19 pandemic: a systematic review

Yasmim mendes rocha.

1 Post-graduate Program in Pharmaceutical Sciences, Federal University of Ceará (UFC), Campus Porangabussu, Fortaleza, CE 60.430-370 Brazil

Gabriel Acácio de Moura

2 Post-graduate Program in Veterinary Sciences, State University of Ceará (UECE), Campus do Itaperi, Fortaleza, CE 60.714-903 Brazil

Gabriel Alves Desidério

3 Health Sciences Institute, University of International Integration of the Afro-Brazilian Lusophony Brazil, CE 060 – Km51, Redençao, CE 62785-000 Brazil

Carlos Henrique de Oliveira

Francisco dantas lourenço, larissa deadame de figueiredo nicolete.

As the new coronavirus disease propagated around the world, the rapid spread of news caused uncertainty in the population. False news has taken over social media, becoming part of life for many people. Thus, this study aimed to evaluate, through a systematic review, the impact of social media on the dissemination of infodemic knowing and its impacts on health.

A systematic search was performed in the MedLine, Virtual Health Library (VHL), and Scielo databases from January 1, 2020, to May 11, 2021. Studies that addressed the impact of fake news on patients and healthcare professionals around the world were included. It was possible to methodologically assess the quality of the selected studies using the Loney and Newcastle–Ottawa Scales.

Fourteen studies were eligible for inclusion, consisting of six cross-sectional and eight descriptive observational studies. Through questionnaires, five studies included measures of anxiety or psychological distress caused by misinformation; another seven assessed feeling fear, uncertainty, and panic, in addition to attacks on health professionals and people of Asian origin.

By analyzing the phenomenon of fake news in health, it was possible to observe that infodemic knowledge can cause psychological disorders and panic, fear, depression, and fatigue.

Introduction

Coronavirus 2019 disease (COVID-19), caused by the SARS-CoV-2 virus, led to the emergence of a pandemic, with a shift in economics, disruption in education, and various rules on home confinement (Munster et al. 2020 ). In this context of uncertainty, there was a need for new information about the virus, clinical manifestations, transmission, and prevention of the disease (Eysenbach 2020 ).

The rapid implementation of these measures, together with the number of significant deaths caused by the virus, ended up causing uncertainty in the population (Tangcharoensathien et al. 2020 ). In association with the generalized panic and the constant concern that COVID-19 caused, this culminated in the appearance of physical and psychological disorders, in addition to reduced immunity in the general population (Lima et al. 2020 ).

Previous studies indicate that the emergence of the pandemic and measures of social confinement caused the number of patients and health professionals with anxiety, sleep disorders and depression to increase; in addition, suicide rates were also considered high (Choi et al. 2020 ; Okechukwu et al. 2020 ). However, the use of social media and search queries to obtain information about the course of the disease is constantly expanding, and includes Twitter, Facebook and Instagram, Google Trends, Bing, Yahoo, and other more popular sources such as blogs, forums, or Wikipedia (Depoux et al. 2020 ).

Thus, information overload accompanied by fabricated and fraudulent news, also called fake news (FN), has emerged in the twentieth century to designate the fake news produced and published by mass communication vehicles such as social media, dominating traditional and social platforms, becoming increasingly part of many people’s daily lives. FNs multiply rapidly and act as narratives that omit or add information to facts (Naeem et al. 2020 ).

The potential effect of FN stems from conspiracy theories, such as a biological weapon produced in China, water with lemon or coconut oil that could kill the virus, or drugs, which even if approved for other indications, could have potential effectiveness in prevention or treatment of COVID-19. Therefore, the impact of this massive dissemination of disease-related information is known as “infodemic knowledge” (Hua and Shaw 2020 ). Other worrisome examples of infodemic knowledge include cases of hydroxychloroquine overdose in Nigeria, drug shortages, changing treatment of patients with rheumatic and autoimmune diseases, and panic over supplies and fuel (CNN 2020 ; Tentolouris et al. 2021 ).

The World Health Organization (WHO 2020 ) has worked closely to track and respond to the most prevalent myths and rumors that can potentially harm public health. In this context, the objective of the study was to evaluate, through a systematic review, the impact of the media and the media during the pandemic caused by the new coronavirus, and to determine how the spread of infodemic impacts people’s health.

This is a systematic literature review that aimed to use explicit and systematic methods to avoid the chance of risk of bias (Donato and Donato 2019 ). Therefore, the study followed a design according to the guidelines of Preferred Report items for Systematic Reviews (PROSPERO) and PRISMA Meta-analyses (PRISMA 2021 ) and the search procedures were filed in the database and registered in PROSPERO: CRD42021256508 (PROSPERO 2021 ).

Searching strategy

Search strategies were developed from the identification of relevant articles using the Medical Subjects Headings (MeSH) in a combination of Boolean AND. The search by string and keyword was calculated as follows: “Covid-19” OR “SARS-CoV-2” AND “fake news” AND “health” OR “Covid-19” AND “fake news” OR “misinformation” AND “health”. The strategy was performed using MedLine, Virtual Health Library (VHL), and Scielo databases. Search results were revised to prevent duplicate studies. The articles obtained were analyzed for relevance and step-by-step, as illustrated in Fig.  1 . The report items for systematic review illustrate the PRISMA (PRISMA 2021 ) process used to report the results.

Search strategy flowchart

Inclusion and exclusion criteria

The search terms were oriented according to the Population, Intervention, Comparison, Results and Study Design (PICOS) approach, methodology used to select the studies included in the systematic search (Methley et al. 2014 ), as shown in Table ​ Table1. 1 . Cross-sectional studies, of cohorts or clinicians that addressed the impact of fake news on patients and health professionals around the world, were used. On the other hand, studies that did not refer to the proposed theme, review articles, or were letters and opinions were excluded. In addition, only full articles written in English, Portuguese (Brazil), and Spanish, published between January 1, 2020, and May 11, 2021, were reviewed.

Approach to study selection (PICO) following systematic search

Database searched in May 2021

Assessment of risk of bias in included studies

Internal quality was performed based on selected study designs using two scales to independently assess the risk of bias; Newcastle–Ottawa for cohort studies and Loney scale for cross-sectional studies. In case of disagreement between two researchers, the assessment was performed by a third experienced researcher (Santos et al. 2019 ). The assessment of the risk of bias between studies was assessed as shown in Tables  2 and ​ and3 3 .

Methodological quality of cross-sectional studies (Loney Scale)

Questions in header relate to different criteria of quality as measured by the Loney Scale:

1 – Is the study design and sampling appropriate to answer the research question? 2 – Is the sample base adequate? 3 – Is the sample size adequate? 4 – Are adequate and standardized objective criteria used to measure motor development? 5 – Was EDM applied in an unbiased way? 6 – Is the response rate adequate? 7 – Were the EDM results presented in a detailed way? 8 – Are participants and context described in detail and can they be generalized to other situations?

Numbers alongside each reference relate to quality of response questions above: 1 = adequate, 2 = inadequate

Methodological quality on the Newcastle–Ottawa Scale (NOS)

Questions in header relate to different criteria of quality as measured by the NOS:

Selection 1: representativeness of the exposed cohort; Selection 2: selection of the unexposed cohort; Selection 3: exposure determination; Selection 4: demonstration that the result of interest was not present at baseline; Comparability 1a and 1b: comparability of cohorts based on design or analysis; Results 1: result evaluation; Results 2: follow-up of cohorts; Results 3: adequacy of cohort follow-up

Data extraction

After collecting data from the articles, they were extracted and tabulated according to the information cited later:

• Type of study.
• Source of FNs.
• Impact of FNs on health.
• Age of participants.
• Country of origin.
• Number of patients.

Study selection

The search strategy identified 1644 publications through the MedLine database, the Virtual Health Library (VHL), and Scielo databases. Of these studies found, 24 were removed for being duplicative and 1606 for being within the exclusion criteria. Based on this, 14 studies met the inclusion criteria and were suitable to be considered in the present review, as shown in Fig. ​ Fig.1 1 .

Study characteristics

Of all the studies included, six were cross-sectional (Ruiz-Frutos et al. 2020 ; Islam et al. 2020 ; Talwar et al. 2020 ; Sallam et al. 2020 ; Duplaga 2020 ; Secosan et al. 2020 ) and eight were descriptive observational studies (Radwan et al. 2020 ; Sun et al. 2020 ; Ahmad and Murad 2020 ; Almomani and Al-Qur’an 2020 ; Roozenbeek et al. 2020 ; Montesi 2020 ; Schmidt et al. 2020 ; Fernández-Torres et al. 2021 ). The sample size of the fourteen selected articles was a total of 571,729 participants, 1467 false new items, and 2508 reports. Most participants were over 18 years of age. The studies were conducted in 14 different countries, including Palestine ( n  = 1), Spain ( n  = 4), India ( n  = 1), Bangladesh ( n  = 1), Iraq ( n  = 1), Mexico ( n  = 1), United States of America ( n  = 1), United Kingdom ( n  = 1), Ireland ( n = 1), Jordan ( n  = 2), China n  = 1), South Africa ( n  = 1), Poland ( n  = 1) and Romania ( n  = 1), each study being able to evaluate more than one country. Other characteristics of the study and the results of the primary study are summarized in Table ​ Table4 4 .

Characteristics of study samples and risk factors associated with fake news

*Possible significant effect of the relationship between fake news and people older than 76 years because they are more likely to be influenced by fake news and to spread such information

The potential risks of misinformation

The results included varied in our review. It was possible to identify that misinformation could trigger varied disturbances to an individual’s perception of FNs. In five papers, the population was observed to be more prone to fearful situations (Talwar et al. 2020 ; Ahmad and Murad 2020 ; Almomani and Al-Qur’an 2020 ; Schmidt et al. 2020 ; Fernández-Torres et al. 2021 ). Consequently, two studies found that a proportion of these patients who reported being afraid because of the influence of FNs reported being confused as to the veracity of this transmitted information (Schmidt et al. 2020 ; Fernández-Torres et al. 2021 ). Our review also found that this situation of fear and confusion can lead to the onset of panic (Talwar et al. 2020 ; Radwan et al. 2020 ; Duplaga 2020 ; Ahmad and Murad 2020 ; Almomani and Al-Qur’an 2020 ; Schmidt et al. 2020 ). In which, the set between the perceptual aspects to these FN can lead to milder symptoms such as fatigue (Islam et al. 2020 ), stress (Secosan et al. 2020 ; Radwan et al. 2020 ), insomnia (Secosan et al. 2020 ), and anger (Radwan et al. 2020 ). The literature also informs us that in addition to milder symptoms inherent to a state of confusion with regard to perceived misinformation conveyed, there is a likelihood of more complex symptomatologies as was reported in five studies with an increase in the number of patients with anxiety (Ruiz-Frutos et al. 2020 ; Sallam et al. 2020 ; Secosan et al. 2020 ; Radwan et al. 2020 ; Sun et al. 2020 ). Patients have also reported being affected by depression processes inherent to these FNs (Secosan et al. 2020 ; Radwan et al. 2020 ).

Susceptibility to spreading fake news according to education and age of the population

To understand the behavior of rumor spreading among the population, our findings reveal that the age of the patients who participated in the study varied mainly between 18 and 60 years, which could infer that a good portion of individuals in different age groups could be susceptible to FN spread through social media. However, in a single study, it was found that people over the age of 76 were more susceptible to being influenced by fake news as well as spreading this information (Sun et al. 2020 ). Another important finding in the literature indicates that susceptibility to interacting with FN is independent of the individual educational level of each study subject, where in four studies it was observed that the patients involved were in secondary school (Duplaga 2020 ; Radwan et al. 2020 ; Sun et al. 2020 ), five studies addressed the susceptibility of undergraduate patients to FN (Sallam et al. 2020 ; Duplaga 2020 ; Secosan et al. 2020 ; Sun et al. 2020 ; Fernández-Torres et al. 2021 ), and in two studies graduate patients were observed (Sun et al. 2020 ; Fernández-Torres et al. 2021 ).

Content and propagation of fake news circulating on social networking platforms

It was possible to verify in the selected articles that the social network Facebook had the greatest participation in the selected studies (Islam et al. 2020 ; Sallam et al. 2020 ; Fernández-Torres et al. 2021 ), followed by Youtube in three studies (Islam et al. 2020 ; Sallam et al. 2020 ; Fernández-Torres et al. 2021 ) and WhatsApp in three more studies (Sallam et al. 2020 ; Radwan et al. 2020 ; Fernández-Torres et al. 2021 ); Twitter appeared in only one study (Sallam et al. 2020 ). Among the main FNs, we had the disclosure that the consumption of food, vitamins, and beverages improved the clinical condition of the affected patient, in addition to reducing the contamination rate (Islam et al. 2020 ; Secosan et al. 2020 ). In other studies, the infection improved with the use of mouthwashes and cutaneous substances (Sun et al. 2020 ; Almomani and Al-Qur’an 2020 ). News related to viral spread, such as the creation of the virus in the laboratory and the spread of the virus by vectors such as mosquitoes, were also addressed (Ahmad and Murad 2020 ; Roozenbeek et al. 2020 ; Montesi 2020 ). Vaccines have also become targets of fake news in studies (Montesi 2020 ).

In the context of the pandemic, the media emerged to seek information about the disease. However, many occurrences were false news masquerading as reliable disease prevention and control strategies, which created an overload of misinformation. In this process, there was interference in the behavior and health of people, generating social unrest associated with violence, distrust, social disturbances, and attacks on health professionals (Moscadelli et al. 2020 ; Apuke and Omar 2021 ).

Overall, our review suggests that people of different nationalities were affected by sharing unverified information. In all the studies included, totaling 1467 news and 2508 reports, the results show that people trust the information they find on social networks, and through these accounts ended up believing and being affected by this information. Only one author pointed out that the news did not represent a danger to people’s health and safety, being considered harmless. This fact was explained by Aleinikov et al. ( 2020 ) pointing out that in this delicate process, the important thing is to relate the perception of risk found in social media and trust in the information provided by institutions (Aleinikov et al. 2020 ).

These tools, while becoming increasingly popular, are also increasingly exposed to unreliable information. As a result, people feel anxious, depressed, or emotionally exhausted, and these expressive health effects are directly associated with the spread of this information (Lin et al. 2020 ). So much so, that when analyzing our data, it was realized that this interaction can come with both mild effects and more serious psychological problems. This is also consistent with the literature, according to Jiang ( 2021 ), who evaluated the possible psychological impact of social media on students during the pandemic and found an increase in the anxiety levels of these students, as well as a worsening in academic performance and physical exhaustion (Jiang 2021 ).

The proliferation of false news has consequences for public health because it fuels panic among people and discredits the scientific community in the eyes of public opinion. For example, a popular myth that consumption of pure alcohol — methanol — could eliminate the virus in the contaminated body killed approximately 800 people in Iran, while another 5876 people were hospitalized for methanol poisoning (Hassanian-Moghaddam et al. 2020 ). As demonstrated in our evaluation, Almomani and Al-Qur’an 2020 and Secosan et al. 2020 , in their reports also claim that the participants, in fact, believed that alcohol consumption cured COVID-19 (Secosan et al. 2020 ; Almomani and Al-Qur’an 2020 ).

Based on the literature, even social media that play a significant role in disseminating true news about COVID-19 have also been linked to illness, because as platforms that help to spread public health messages to people, they also promote opinionated reporting. and concerns about the disease (Galea et al. 2020 ). In fact, the results pointed out in this review reveal that 36% of the authors showed that exposure to infodemic knowledge generated fear, panic, depression, stress, and anxiety in people interviewed through an online questionnaire. This is corroborated by a cross-sectional study carried out by Olagoke et al. ( 2020 ), that when evaluating 501 participants, the anxiety and depression score was related to news exposure in the traditional media, showing a prevalence of depressive symptoms and a greater perceived vulnerability, causing great psychological impact.

Our results indicate that different age groups have susceptibility to interact with the FN propagated by social media, especially in the elderly population. These results were also verified in a previous study by Guimarães et al. 2021 , who aimed to assess the population’s knowledge about COVID-19 and misinformation from an anonymous online survey and, with this, some parameters such as gender, education, and age were shown to be directly associated with a better perception of health issues in the context of the pandemic (Guimarães et al. 2021 ). The same was also seen by Hayat et al. 2020 , who explored the public’s understanding of the current situation of the COVID-19 from online forms and concluded that participants with ages ranging from 16 to 29 years obtained better scores than older participants (Hayat et al. 2020 ). Such a fact is associated with the digital media literacy of individuals primarily over the age of 60 who end up not reliably determining the trustworthiness of online news, thus needing to develop literacy competencies that encompass the types of skills needed to identify questionable content (Guess et al. 2019 ).

To understand the behavior of spreading rumors among the elderly population, our results show that most respondents (74.82%) negatively evaluated the dissemination of fake news, while 2.52% did not care anyway. Among them, the correlation between the spread of rumors and anxiety was negatively associated, as they influence the behavior and perception of the elderly to understand what a fact is and what is fake news. Research shows that individuals over 65 years share up to seven times more unverified information when compared to other age groups, often in order to feel useful, active, and connected (Guess et al. 2019 ). Certainly, psychological interventions are mainly recommended to vulnerable populations and health professionals (Van Der Linden et al. 2020 ).

Our results also showed that 36% of the authors reported that, regardless of age, it was possible for participants to experience fatigue, anguish, and psychological distress, in addition to having a higher probability of developing anxiety-related symptoms. This is contradicted in two previous studies by Huang and Zhao ( 2020 ) and Wang et al. ( 2020 ); when evaluating the psychological impact of the uncontrolled spread of COVID-19, they realized that the manifestations of anxiety and psychological outbreaks were more common especially in the younger population who used social networks for a longer time (Huang and Zhao 2020 ; Wang et al. 2020 ). On the other hand, pandemic uncertainty and confinement created considerable levels of stress in young people, especially women, in Switzerland (Mohler-Kuo et al. 2021 ). It was further shown that misinformation fueled by rumors and conspiracy theories led to physical harassment and violent attacks against healthcare professionals and people of Asian origin in 28% of the results shown in this review. This finding is in line with a study that shows that conspiracy theories are not a new phenomenon, but they increase in times of crisis. Thus, people who believe in this “conspiracy world” are less likely to comply with social norms (Imhoff and Lamberty 2020 ).

The impact of denial and its association with fake news presents itself as a social phenomenon through the production of controversial theses to the scientific consensus (Duarte and César 2020 ). Good examples of denial content can be the emergence of the earthmoving movement, the global warming farce, and anti-vaccination discourses (Vasconcelos-Silva and Castiel 2020 ). With regard to the COVID-19 pandemic, denialism takes on an expression never seen before, in which the number of people who spread this news grows more and more, and therefore results in an increase in the number of deaths of the most vulnerable patients (Morel 2021 ).

Importantly, false information has been a genuine concern among social-media platforms and governments, which have implemented strategies to contain misinformation and fake news during the pandemic. Of the social-media platforms, in order to contain the advance of FNs, Facebook has implemented a new feature to inform users when they engage with unverified information (BBC 2020 ). Another way to counteract misinformation is to seek support and discuss actions that authorities or public agencies could take to mitigate the spread of conspiracy theories, and encourage users to flag inappropriate content to social-media companies (González-Padilla and Tortolero-Blanco 2020 ).

Social-media platforms have contributed to the spread of false news and conspiracy theories during the new coronavirus pandemic. When analyzing the phenomenon of fake news in health, it is possible to observe that infodemic knowledge is part of people’s lives around the world, causing distrust in Governments, researchers, and health professionals, which can directly impact people’s lives and health. When analyzing the potential risks of misinformation, panic, depression, fear, fatigue, and the risk of infection influence psychological distress and emotional overload. In the COVID-19 pandemic, the disposition to spread incorrect information or rumors is directly related to the development of anxiety in populations of different ages.

Acknowledgments

The authors would like to thank the CAPES and FUNCAP for the fellowships of Yasmim M Rocha and Gabriel A de Moura.

Author contributions

Yasmim Mendes Rocha: bibliographic research, concepts, methodology, writing, and data analysis. Gabriel Acácio de Moura: bibliographic research, methodology, revision, editing, and data analysis. Gabriel Alves Desidério: reading of included articles and review. Carlos Henrique de Oliveira: translation into English, reading of articles, and writing. Francisco Dantas Lourenço: article reading and review. Larissa Deadame de Figueiredo Nicolete: article idea, supervision, methodology, research, formal analysis, and editing.

This study was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) and the Cearense Foundation for Scientific and Technological Development Support (FUNCAP).

Declarations

The authors declare no conflict of interest.

This is a review article that has not been published before and is not being considered for publication anywhere. Authors confirm that the manuscript has been read and approved by all named authors and that no other person has met the authorship criteria, but it is not listed. We further confirm that none of us have any conflict of interest to declare. We would like to thank you for your attention and the opportunity given to submit our study, and agree, if the manuscript is accepted for publication, to the transfer of all copyright to the Journal of Public Health .

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‘Fake news’ – why people believe it and what can be done to counter it

Director Institute of Cultural Capital, University of Liverpool

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Barack Obama believes “fake news” is a threat to democracy. The outgoing US president said he was worried about the way that “so much active misinformation” can be “packaged very well” and presented as fact on people’s social media feeds. He told a recent conference in Germany:

If we are not serious about facts and what’s true and what’s not, if we can’t discriminate between serious arguments and propaganda, then we have problems.

But how do we distinguish between facts, legitimate debate and propaganda? Since the Brexit vote and the Donald Trump victory a huge amount of journalists’ ink has been used up discussing the impact of social media and the spread of “ fake news ” on political discourse, the functioning of democracy and on journalism. Detailed social science research is yet to emerge, though a lot can be learnt from existing studies of online and offline behaviour.

Matter of trust

Let’s start with a broad definition of “fake news” as information distributed via a medium – often for the benefit of specific social actors – that then proves unverifiable or materially incorrect. As has been noted, “fake news” used to be called propaganda. And there is an extensive social science literature on propaganda , its history, function and links to the state – both democratic and dictatorial.

In fact, as the investigations in the US and Italy show, one of the major sources of fake news is Russia. Full Fact , a site in the UK, is dedicated to rooting out media stories that play fast and loose with the truth – and there is no shortage.

An argument could be made that as the “mainstream” media have become seen as less trustworthy (rightly or wrongly) in the eyes of their audiences, it makes it hard to distinguish between those who have supposedly got a vested interest in telling the truth and those that don’t necessarily share the same ethical foundation. How does mainstream journalism that is also clearly politically biased – on all sides – claim the moral high ground? This problem certainly predates digital technology.

Bubbles and echo chambers

This leaves us with the question of whether social media makes it worse? Almost as much ink has been used up talking about social media “bubbles” – how we all tend to talk with people who share our outlook – something, again, which is not necessarily unique to the digital age. This operates in two distinct ways.

Bubbles are a product of class and cultural position. A recent UK study on social class pointed this out. An important subtlety here is that though those with higher “social status” may congregate, they are also likely to have more socially diverse acquaintance networks than those in lower income and status groups. They are also likely to have a greater diversity of media, especially internet usage patterns . Not all bubbles are the same size nor as monochromatic and our social media bubbles reflect our everyday “offline” bubbles .

In fact social media bubbles may be very pertinent to journalist-politician interactions as one of the best-defined Twitter bubbles is the one that surrounds politicians and journalists.

This brings back into focus older models of media effects such as the two-step flow model where key “opinion leaders” – influential nodes in our social networks – have an impact on our consumption of media. Analyses of a “fake news story” appears to point – not to social media per se – but to how stories moving through social media can be picked up by leading sites and actors with many followers and become amplified.

The false assumption in a tweet from an individual becomes a “fake news” story on an ideologically-driven news site or becomes a tweet from the president-elect and becomes a “fact” for many. And we panic more about this as social media make both the message and how it moves very visible.

Outing fake news

What fuels this and can we address it? First, the economics of social media favour gossip, novelty, speed and “shareability”. They mistake sociability for social value. There is evidence that “fake news” that plays to existing prejudice is more likely to be “liked” and so generate more revenue for the creators. This is no different than “celebrity” magazines. Well researched and documented news is far less likely to be widely shared.

The other key point here is that – as Obama noted – it becomes hard to distinguish fake from fact, and there is evidence that many struggle to do this. As my colleagues and I argued nearly 20 years ago , digital media make it harder to distinguish the veracity of content simply by the physical format it comes in (broadsheet newspaper, high-quality news broadcast, textbook or tabloid story). Online news is harder to distinguish.

The next problem is that retracting “fake news” on social media is currently poorly supported by the technology. Though posts can be deleted, this is a passive act, less impactful than even the single-paragraph retractions in newspapers . In order to have an impact, it would be necessary not simply to delete posts but to highlight and require users to see and acknowledge items removed as “fake news”.

So whether or not fake news is a manifestation of the digital and social media age, it seems likely that social media is able to amplify the spread of misinformation. Their economics favour shareability over veracity and distribution over retraction. These are not technology “requirements” but choices – by the systems’ designers and their regulators (where there are any). And mainstream media may have tarnished their own reputation through “fake” and visibly ideological news coverage, opening the door to other news sources.

Understanding this complex mix of factors is the job of the social sciences. But maybe the real message here is that we as societies and individuals have questions to answer about educating people to read the news, about our choice not to regulate social media (as we do TV and print) and in our own behaviour – ask yourself, how often do you fact-check a story before reposting it?

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• DOI: 10.1080/1369118X.2021.2000005
• Corpus ID: 244500560

Review essay: fake news, and online misinformation and disinformation

• Andrew White
• Published in Information, Communication… 22 November 2021
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One Citation

Evaluating the effectiveness of hybrid features in fake news detection on social media, 27 references, fake news : understanding media and misinformation in the digital age, ‘a war against truth’ - understanding the fake news controversy, viral “fake news” lists and the limitations of labeling and fact-checking, combating the sharing of false information: history, framework, and literacy strategies, the structural transformation of the public sphere : an inquiryinto a category of bourgeois society, lie machines.

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The Right Way to Fight Fake News

Social media platforms need to make sure their anti-misinformation strategies are empirically grounded.

By Gordon Pennycook and David Rand

Dr. Pennycook and Dr. Rand are cognitive psychologists.

Social media companies have been under tremendous pressure since the 2016 presidential election to do something — anything — about the proliferation of misinformation on their platforms.

Companies like Facebook and YouTube have responded by applying anti-fake-news strategies that seem as if they would be effective. As a public-relations move, this is smart: The companies demonstrate that they are willing to take action, and the policies sound reasonable to the public.

But just because a strategy sounds reasonable doesn’t mean it works. Although the platforms are making some progress in their fight against misinformation, recent research by us and other scholars suggests that many of their tactics may be ineffective — and can even make matters worse, leading to confusion, not clarity, about the truth. Social media companies need to empirically investigate whether the concerns raised in these experiments are relevant to how their users are processing information on their platforms.

One strategy that platforms have used is to provide more information about the news’ source. YouTube has “information panels” that tell users when content was produced by government-funded organizations, and Facebook has a “context” option that provides background information for the sources of articles in its News Feed. This sort of tactic makes intuitive sense because well-established mainstream news sources, though far from perfect, have higher editing and reporting standards than, say, obscure websites that produce fabricated content with no author attribution.

But recent research of ours raises questions about the effectiveness of this approach. We conducted a series of experiments with nearly 7,000 Americans and found that emphasizing sources had virtually no impact on whether people believed news headlines or considered sharing them.

People in these experiments were shown a series of headlines that had circulated widely on social media — some of which came from mainstream outlets such as NPR and some from disreputable fringe outlets like the now-defunct newsbreakshere.com. Some participants were provided no information about the publishers, others were shown the domain of the publisher’s website, and still others were shown a large banner with the publisher’s logo. Perhaps surprisingly, providing the additional information did not make people much less likely to believe misinformation.

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The researchers sought to understand how the reward structure of social media sites drives users to develop habits of posting misinformation on social media. (Photo/AdobeStock)

USC study reveals the key reason why fake news spreads on social media

The USC-led study of more than 2,400 Facebook users suggests that platforms — more than individual users — have a larger role to play in stopping the spread of misinformation online.

USC researchers may have found the biggest influencer in the spread of fake news: social platforms’ structure of rewarding users for habitually sharing information.

The team’s findings, published Monday by Proceedings of the National Academy of Sciences , upend popular misconceptions that misinformation spreads because users lack the critical thinking skills necessary for discerning truth from falsehood or because their strong political beliefs skew their judgment.

Just 15% of the most habitual news sharers in the research were responsible for spreading about 30% to 40% of the fake news.

The research team from the USC Marshall School of Business and the USC Dornsife College of Letters, Arts and Sciences wondered: What motivates these users? As it turns out, much like any video game, social media has a rewards system that encourages users to stay on their accounts and keep posting and sharing. Users who post and share frequently, especially sensational, eye-catching information, are likely to attract attention.

“Due to the reward-based learning systems on social media, users form habits of sharing information that gets recognition from others,” the researchers wrote. “Once habits form, information sharing is automatically activated by cues on the platform without users considering critical response outcomes, such as spreading misinformation.”

Posting, sharing and engaging with others on social media can, therefore, become a habit.

“[Misinformation is] really a function of the structure of the social media sites themselves.” — Wendy Wood , USC expert on habits

“Our findings show that misinformation isn’t spread through a deficit of users. It’s really a function of the structure of the social media sites themselves,” said Wendy Wood , an expert on habits and USC emerita Provost Professor of psychology and business.

“The habits of social media users are a bigger driver of misinformation spread than individual attributes. We know from prior research that some people don’t process information critically, and others form opinions based on political biases, which also affects their ability to recognize false stories online,” said Gizem Ceylan, who led the study during her doctorate at USC Marshall and is now a postdoctoral researcher at the Yale School of Management . “However, we show that the reward structure of social media platforms plays a bigger role when it comes to misinformation spread.”

In a novel approach, Ceylan and her co-authors sought to understand how the reward structure of social media sites drives users to develop habits of posting misinformation on social media.

Why fake news spreads: behind the social network

Overall, the study involved 2,476 active Facebook users ranging in age from 18 to 89 who volunteered in response to online advertising to participate. They were compensated to complete a “decision-making” survey approximately seven minutes long.

Surprisingly, the researchers found that users’ social media habits doubled and, in some cases, tripled the amount of fake news they shared. Their habits were more influential in sharing fake news than other factors, including political beliefs and lack of critical reasoning.

Frequent, habitual users forwarded six times more fake news than occasional or new users.

“This type of behavior has been rewarded in the past by algorithms that prioritize engagement when selecting which posts users see in their news feed, and by the structure and design of the sites themselves,” said second author Ian A. Anderson , a behavioral scientist and doctoral candidate at USC Dornsife. “Understanding the dynamics behind misinformation spread is important given its political, health and social consequences.”

Experimenting with different scenarios to see why fake news spreads

In the first experiment, the researchers found that habitual users of social media share both true and fake news.

In another experiment, the researchers found that habitual sharing of misinformation is part of a broader pattern of insensitivity to the information being shared. In fact, habitual users shared politically discordant news — news that challenged their political beliefs — as much as concordant news that they endorsed.

Lastly, the team tested whether social media reward structures could be devised to promote sharing of true over false information. They showed that incentives for accuracy rather than popularity (as is currently the case on social media sites) doubled the amount of accurate news that users share on social platforms.

The study’s conclusions:

• Habitual sharing of misinformation is not inevitable.
• Users could be incentivized to build sharing habits that make them more sensitive to sharing truthful content.
• Effectively reducing misinformation would require restructuring the online environments that promote and support its sharing.

These findings suggest that social media platforms can take a more active step than moderating what information is posted and instead pursue structural changes in their reward structure to limit the spread of misinformation.

About the study:  The research was supported and funded by the USC Dornsife College of Letters, Arts and Sciences Department of Psychology, the USC Marshall School of Business and the Yale University School of Management.

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Expert Commentary

Fake news and fact-checking: 7 studies you should know about

We spotlight seven research studies published in 2019 that examine fake news from multiple angles, including what makes fact-checking most effective.

Republish this article

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by Denise-Marie Ordway, The Journalist's Resource January 13, 2020

This <a target="_blank" href="https://journalistsresource.org/politics-and-government/fake-news-fact-checking-research-2019/">article</a> first appeared on <a target="_blank" href="https://journalistsresource.org">The Journalist's Resource</a> and is republished here under a Creative Commons license.<img src="https://journalistsresource.org/wp-content/uploads/2020/11/cropped-jr-favicon-150x150.png" style="width:1em;height:1em;margin-left:10px;">

What better way to start the new year than by learning new things about how best to battle fake news and other forms of online misinformation? Below is a sampling of the research published in 2019 — seven journal articles that examine fake news from multiple angles, including what makes fact-checking most effective and the potential use of crowdsourcing to help detect false content on social media.

Because getting good news is also a great way to start 2020, I included a study that suggests President Donald Trump’s “fake news” tweets aimed at discrediting news coverage could actually help journalists. The authors of that paper recommend journalists “engage in a sort of news jujitsu, turning the negative energy of Trump’s tweets into a force for creating additional interest in news.”

This article was first published at  Nieman Lab .

“Real solutions for fake news? Measuring the effectiveness of general warnings and fact-check tags in reducing belief in false stories on social media” : From Dartmouth College and the University of Michigan, published in Political Behavior. By Katherine Clayton, Spencer Blair, Jonathan A. Busam, Samuel Forstner, John Glance, Guy Green, Anna Kawata, Akhila Kovvuri, Jonathan Martin, Evan Morgan, Morgan Sandhu, Rachel Sang, Rachel Scholz‑Bright, Austin T. Welch, Andrew G. Wolff, Amanda Zhou, and Brendan Nyhan.

This study provides several new insights about the most effective ways to counter fake news on social media. Researchers found that when fake news headlines were flagged with a tag that says “Rated false,” people were less likely to accept the headline as accurate than when headlines carried a “Disputed” tag. They also found that posting a general warning telling readers to beware of misleading content could backfire. After seeing a general warning, study participants were less likely to believe true headlines and false ones.

The authors note that while their sample of 2,994 U.S. adults isn’t nationally representative, the feedback they got demonstrates that online fake news can be countered “with some degree of success.” “The findings suggest that the specific warnings were more effective because they reduced belief solely for false headlines and did not create spillover effects on perceived accuracy of true news,” they write.

“Fighting misinformation on social media using crowdsourced judgments of news source quality” : From the University of Regina and Massachusetts Institute of Technology, published in the Proceedings of the National Academy of Sciences. By Gordon Pennycook and David G. Rand.

It would be time-consuming and expensive to hire crowds of professional fact-checkers to find and flag all the false content on social media. But what if the laypeople who use those platforms pitched in? Could they accurately assess the trustworthiness of news websites, even if prior research indicates they don’t do a good job judging the reliability of individual news articles? This research article, which examines the results of two related experiments with almost 2,000 participants, finds the idea has promise.

“We find remarkably high agreement between fact-checkers and laypeople,” the authors write. “This agreement is largely driven by both laypeople and fact-checkers giving very low ratings to hyper-partisan and fake news sites.”

The authors note that in order to accurately assess sites, however, people need to be familiar with them. When news sites are new or unfamiliar, they’re likely to be rated as unreliable, the authors explain. Their analysis also finds that Democrats were better at gauging the trustworthiness of media organizations than Republicans — their ratings were more similar to those of professional fact checkers. Republicans were more distrusting of mainstream news organizations.

“All the president’s tweets: Effects of exposure to Trump’s ‘fake news’ accusations on perceptions of journalists, news stories, and issue evaluation” : From Virginia Tech and EAB, published in Mass Communication and Society. By Daniel J. Tamul, Adrienne Holz Ivory, Jessica Hotter, and Jordan Wolf.

When Trump turns to Twitter to accuse legitimate news outlets of being “fake news,” does the public’s view of journalists change? Are people who read his tweets less likely to believe news coverage? To investigate such questions, researchers conducted two studies, during which they showed some participants a sampling of the president’s “fake news” tweets and asked them to read a news story.

Here’s what the researchers learned: The more tweets people chose to read, the greater their intent to read more news in the future. As participants read more tweets, their assessments of news stories’ and journalists’ credibility also rose. “If anything, we can conclude that Trump’s tweets about fake news drive greater interest in news more generally,” the authors write.

The authors’ findings, however, cannot be generalized beyond the individuals who participated in the two studies — 331 people for the first study and then 1,588 for the second, more than half of whom were undergraduate students.

Based on their findings, the researchers offer a few suggestions for journalists. “In the short term,” they write, “if journalists can push out stories to social media feeds immediately after Trump or others tweet about legitimate news as being ‘fake news,’ then practitioners may disarm Trump’s toxic rhetoric and even enhance the perceived credibility of and demand for their own work. Using hashtags, quickly posting stories in response to Trump, and replying directly to him may also tether news accounts to the tweets in social media feeds.”

“Who shared it?: Deciding what news to trust on social media” : From NORC at the University of Chicago and the American Press Institute, published in Digital Journalism. By David Sterrett, Dan Malato, Jennifer Benz, Liz Kantor, Trevor Tompson, Tom Rosenstiel, Jeff Sonderman, and Kevin Loker.

This study looks at whether news outlets or public figures have a greater influence on people’s perception of a news article’s trustworthiness. The findings suggest that when a public figure such as Oprah Winfrey or Dr. Oz shares a news article on social media, people’s attitude toward the article is linked to how much they trust the public figure. A news outlet’s reputation appears to have far less impact.

In fact, researchers found mixed evidence that audiences will be more likely to trust and engage with news if it comes from a reputable news outlet than if it comes from a fake news website. The authors write that “if people do not know a [news outlet] source, they approach its information similarly to how they would a [news outlet] source they know and trust.”

The authors note that the conditions under which they conducted the study were somewhat different from those that participants would likely encounter in real life. Researchers asked a nationally representative sample of 1,489 adults to read and answer questions about a simulated Facebook post that focused on a news article, which appeared to have been shared by one of eight public figures. In real life, these adults might have responded differently had they spotted such a post on their personal Facebook feeds, the authors explain.

Still, the findings provide new insights on how people interpret and engage with news. “For news organizations who often rely on the strength of their brands to maintain trust in their audience, this study suggests that how people perceive their reporting on social media may have little to do with that brand,” the authors write. “A greater presence or role for individual journalists on social networks may help them boost trust in the content they create and share.”

“Trends in the diffusion of misinformation on social media” : From New York University and Stanford University, published in Research and Politics. By Hunt Allcott, Matthew Gentzkow, and Chuan Yu.

This paper looks at changes in the volume of misinformation circulating on social media. The gist: Since 2016, interactions with false content on Facebook have dropped dramatically but have risen on Twitter. Still, lots of people continue to click on, comment on, like and share misinformation.

The researchers looked at how often the public interacted with stories from 569 fake news websites that appeared on Facebook and Twitter between January 2015 and July 2018. They found that Facebook engagements fell from about 160 million a month in late 2016 to about 60 million a month in mid-2018. On Twitter, material from fake news sites was shared about 4 million times a month in late 2016 and grew to about 5 million shares a month in mid-2018.

The authors write that the evidence is “consistent with the view that the overall magnitude of the misinformation problem may have declined, possibly due to changes to the Facebook platform following the 2016 election.”

“Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning” : From Yale University, published in Cognition. By Gordon Pennycook and David G. Rand.

This study looks at the cognitive mechanisms behind belief in fake news by investigating whether fake news has gained traction because of political partisanship or because some people lack strong reasoning skills. A key finding: Adults who performed better on a cognitive test were better able to detect fake news, regardless of their political affiliation or education levels and whether the headlines they read were pro-Democrat, pro-Republican or politically neutral. Across two studies conducted with 3,446 participants, the evidence suggests that “susceptibility to fake news is driven more by lazy thinking than it is by partisan bias per se,” the authors write.

The authors also discovered that study participants who supported Trump had a weaker capacity for differentiating between real and fake news than did those who supported 2016 presidential candidate Hillary Clinton. The authors write that they are not sure why that is, but it might explain why fake news that benefited Republicans or harmed Democrats seemed more common before the 2016 national election.

“Fact-checking: A meta-analysis of what works and for whom” : From Northwestern University, University of Haifa, and Temple University, published in Political Communication. By Nathan Walter, Jonathan Cohen, R. Lance Holbert, and Yasmin Morag.

Even as the number of fact-checking outlets continues to grow globally, individual studies of their impact on misinformation have provided contradictory results. To better understand whether fact-checking is an effective means of correcting political misinformation, scholars from three universities teamed up to synthesize the findings of 30 studies published or released between 2013 and 2018. Their analysis reveals that the success of fact-checking efforts varies according to a number of factors.

The resulting paper offers numerous insights on when and how fact-checking succeeds or fails. Some of the big takeaways:

• Fact-checking messages that feature graphical elements such as so-called “truth scales” tended to be less effective in correcting misinformation than those that did not. The authors point out that “the inclusion of graphical elements appears to backfire and attenuate correction of misinformation.”
• Fact-checkers were more effective when they tried to correct an entire statement rather than parts of one. Also, according to the analysis, “fact-checking effects were significantly weaker for campaign-related statements.”
• Fact-checking that refutes ideas that contradict someone’s personal ideology was more effective than fact-checking aimed at debunking ideas that match someone’s personal ideology.
• Simple messages were more effective. “As a whole, lexical complexity appears to detract from fact-checking efforts,” the authors explain.

Interested in research on fake news and digital media from previous years? Please check out our research roundups from 2018 and 2017 .

This image was obtained from the Flickr account of Alan Levine and is being used under a Creative Commons license . No changes were made.

About The Author

Denise-Marie Ordway

Essay on Effect of Fake News on Social Media

Students are often asked to write an essay on Effect of Fake News on Social Media in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

100 Words Essay on Effect of Fake News on Social Media

Introduction.

Fake news refers to misinformation or false stories spread on social media. It’s a serious problem affecting the credibility of information online.

Impact on Society

Fake news can cause panic and confusion. It can influence public opinion, leading to harmful decisions or actions.

Role of Social Media

To combat fake news, it’s crucial to verify information before sharing and report suspected fake news to social media platforms.

250 Words Essay on Effect of Fake News on Social Media

The proliferation of fake news.

The accessibility and anonymity of social media platforms make them a convenient tool for spreading fake news. Misinformation can be created and shared with a few clicks, reaching millions within seconds. The lack of stringent fact-checking mechanisms further exacerbates this issue.

Impacts on Public Perception and Behavior

Fake news can distort public perception, fueling fear, bias, and misunderstanding. It can influence political views, incite violence, and even impact public health decisions, as seen during the COVID-19 pandemic.

The Role of Algorithms

The effect of fake news on social media is profound and multifaceted. It underscores the urgent need for improved digital literacy, robust fact-checking mechanisms, and algorithmic transparency. As we move further into the digital age, these issues will require ongoing attention and innovative solutions.

500 Words Essay on Effect of Fake News on Social Media

Fake news, a term frequently used in recent years, refers to fabricated information that mimics news media content in form but not in organizational process or intent. The proliferation of fake news, especially on social media, has become a significant concern due to its potential to manipulate public opinion, incite hatred, and even influence elections. This essay explores the effects of fake news on social media, focusing on its implications for society, politics, and the economy.

The Social Impact of Fake News

Political consequences of fake news.

The political implications of fake news are equally alarming. By manipulating public opinion, fake news can influence electoral outcomes and undermine democratic processes. The 2016 US elections are a case in point, where fake news played a significant role in shaping public opinion. Moreover, fake news can erode trust in institutions and leaders by spreading conspiracy theories and misinformation. This distrust can destabilize political systems and lead to social unrest.

Economic Implications of Fake News

The economic effects of fake news are often overlooked but can be substantial. Fake news can influence stock markets, as investors may make decisions based on false information. Additionally, companies can suffer reputational damage due to fake news, leading to financial losses. Furthermore, the resources spent on combating fake news represent a significant economic cost.

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Home / Essay Samples / Sociology / Fake News / Effect of Fake News on Social Media: Definition and Prevention

Effect of Fake News on Social Media: Definition and Prevention

• Category: Information Science and Technology , Sociology
• Topic: Digital Literacy , Effects of Social Media , Fake News

Pages: 3 (1519 words)

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