User:Idmanii/sandbox
The Liar’s Dividend
[edit]The Liar’s Dividend is a concept in which the spread of believable misinformation, results in lack of trust in authentic evidence, leading to the belief that all information presented is untrue.[1] Coined by Robert Chesney and Danielle Citron in 2018, this term presents how false information allows individuals to dismiss real evidence as ‘fake’.[1] Often to benefit their own personal or political interests.[1] This leads to a situation where it is difficult for the truth to be distinguished.[2]
Several psychological principles outline the drive behind the liar’s dividend. Confirmation bias motivates individuals to seek out information that aligns with their pre-existing beliefs and ignore opposing evidence.[3] Cognitive dissonance occurs when individuals avoid the discomfort that arises from beliefs that confront their views.[4] Group polarisation creates a context in which discussions within a group, reinforces a member’s established beliefs.[5]
The liar’s dividend creates a feedback loop where false information undermines the credibility of accurate information, leading to distrust in all information presented.[6] This impacts society, media and politics through various psychological mechanisms.
Psychological Principles of the Liar’s Dividend
[edit]Confirmation Bias
[edit]Confirmation bias is the cognitive tendency to filter information selectively to resonate with one’s prior beliefs, while downplaying information that challenges this.[7] This mechanism can distort viewpoints and lead to resistance in understanding others’ perceptions.[8] The attachment individuals have to their perspectives and reluctance to adapt them, can have potential issues such as social inequality.[9]
This bias reinforces the liar’s dividend by increasing doubt in authentic evidence that counters individuals pre-existing belief.[10] The selective doubt diminishes confidence in the truth, which is the key element of liar’s dividend, thereby enhancing false narratives and misinformation.[11]
Civil Rights Movement
[edit]A historical example of this is the Civil Rights Movement in the United States, which advocated for equal rights for African Americans.[12] Confirmation bias perpetuated racial inequality and social injustice faced during this time.[13] Established societal beliefs, hindered the acknowledgment of the system of oppression.[14]
This is interconnected with the liar’s dividend. Dismissing the institutionalised racism, allowed false ideologies to spread and created scepticism around the objectives of the Civil Right Movement.[15] In turn, amplifying the ability for further false narratives to occur, devaluing the true reasons behind this movement.[15] Thus, contributing to the growth of liar’s dividend and social inequality.
Cognitive Dissonance
[edit]Cognitive dissonance is a psychological state that arises when a person is presented with viewpoints that do not align with their own beliefs.[4] This state is aversive; to avoid the discomfort they refute opposing information.[4]
The liar’s dividend has strong links to cognitive dissonance: when individuals ignore authentic evidence, it contributes to the spread of inaccurate narratives, strengthening misinformation and amplifies the liar’s dividend.[2]
Climate Change Denial
[edit]Almost 15% of the United States population does not believe climate change is real.[16] This dilemma can be explained through cognitive dissonance. Though various research has demonstrated that the global temperature is increasing, conflicting information produces mental discomfort, that can be avoided by dismissing the truth. [17]As a result, credible evidence is dismissed , contributing to climate change denial.
The liar’s dividend thrives under these conditions, in which misinformation circulates, strengthening falsehoods about climate change.[2] In turn, the lack of trust and increased doubt in scientific evidence, hinders efforts to mitigate climate change.
When Prophecy Fails
[edit]In Festinger's (1956) study (when prophecy fails) he infiltrated a doomsday cult, to demonstrate how cognitive dissonance strengthens an individual’s belief.[18] This process enhances the liar’s dividend, as the continued strengthening of false portrayals, weakens the trust in authentic evidence.[1] Obscuring the truth, allowing deception to strengthen.
Group Polarisation
[edit]Group polarisation occurs when shared exchanges heightens an attitude amongst the in-group, that would not have been endorsed to such an extreme if individual’s were alone. [5]This phenomenon thrives within echo chambers, environments that favours and enhances individual's own views.[19] Isolating the possibility of other perspectives, thus reinforcing their core beliefs.[19]
This isolation results in people becoming more inclined to discuss views they know will be endorsed by the group, even if proven to be false.[20] This creates the liar’s dividend, making the truth seem doubtful and allows the spread of inaccuracies. [21]A lack of objectivity within the group, amplifies misinformation and undermines credible information.[21]
Anti-vaccine Activism
[edit]A prime example of this is anti-vaccine activism, which claims that vaccinations are part of a government scheme. [22]The Microchip Surveillance Theory arose during the COVID-19 pandemic, alleging that there were microchips imbedded in the vaccines to allow the government to control the human population.[23] Echo chambers within this cohort, catalysed the spread of this misinformation.[24]
The dismissal of authentic evidence disproving the theory and the misrepresentation, creates scepticism around vaccinations, promoting the development of the liar’s dividend.[25] This creates an environment in which false information around vaccinations, weakens the legitimacy of the truth, making it challenging for factual evidence to be accepted.[26]
Role of Social Media
[edit]As of 2024, 63% of people worldwide use social media, with platforms such as X (previously known as Twitter), Facebook, YouTube and Instagram being the most common.[27] [28]These platforms have been the face of many different forms of the liar’s dividend through the exploitation of social divisions.[29] Algorithms within these social media platforms focus on providing content that resonate with users’ established beliefs, meaning the credibility of a source is not taken into account. [30] This creates a cycle in which alternative views on social media are perceived as less trustworthy, allowing false claims to persist, in turn perpetuating the liar’s dividend.[31]
#BuildTheWall was a political message that gained widespread attention on Twitter in 2016 during Donald Trump’s presidential campaign. [32] It derived from the We Build the Wall campaign, which aimed to create a physical boarder between the United States and Mexico, demonstrating how social media can shape political discourse.[33] This scheme increased social divisions that were prominent at the time, by creating a digital space where individuals’ beliefs were intensified and validated by others with similar perspectives.[34]
Digital spaces also reinforce individuals’ views outside of social media, intensifying the divide between ‘us’ and ‘them’.[35] The political polarisation on Twitter, meant that those advocating for #BuildTheWall become confined within echo chambers, leading to the persist affirmation of their beliefs from group members..[35]
As a result, when contradictory information is presented, it was often rejected, allowing misinformation to thrive and the liar’s dividend to take effect.[36] For instance, the false allegation that illegal immigrants were the cause of high crime rates in the United States, contributed to validating the We Build the Wall scheme.[37]
Though the American Immigration Council providing authentic evidence to clarify misrepresentations, showing that immigrants have lower crime rates than U.S born citizens.[38] The false claims continued to persist across social media and the truth was dismissed.[2]
The societal implications of the liar’s dividend is amplified through social media, as well as the difficulty to address the inaccurate claims.
YouTube
[edit]YouTube is one of the most popular social media apps used today, which has given it the ability to sway people’s opinions.[39] An example of this is the Flat Earth Movement, which has increased in attention through YouTube.[40] The algorithm of this app is designed to recommend similar content to the user.[41] As a result, even those engaging with such content out of curiosity, may be presented with a feed that is oriented to the beliefs of the Flat Earth Movement.[42]
The widespread inaccuracies, bolsters the effect of falsehoods, fostering the liar’s dividend. Though contradictory evidence, in particular satellite images, illustrate that the claim of a ‘flat earth’ is inaccurate, YouTube’s platform creates a space that limits exposure to diverse views.[43] Thus, enhancing the views the algorithm is designed to optimise, facilitating the growth of widespread inaccuracies and limiting credibility of the truth, driving the liar’s dividend. [44]
Nonetheless, it remains the individual's duty to critically analyse the information presented to them, having an active role in identifying falsehoods.[45]
Case studies of the Liar’s Dividend
[edit]US Presidential Elections
[edit]The extensive spread of fake news was a notable factor in multiple U.S. Presidential Elections, until the line between fake and authentic news was hard to detect.[46] In November of 2016, Trump took to Twitter, stating ‘I won the popular vote if you deduct the millions of people who voted illegally’.[47] This false claim of election fraud across various electoral periods, contributed to scepticism around the authenticity of the election.[48] In these circumstances, many supporters were likely to believe such claims, as they aligned with their prior beliefs (confirmation bias).[49]Therefore, these allegations validated the narratives and when presented with evidence that devalues the claims, it enhanced their beliefs (cognitive bias). Such claims allowed misinformation to prosper, diminishing the legitimacy of real evidence creating an environment for further false claims to be made. Until real evidence is dismissed and fake news becomes the primary source of information retrieval, giving rise to liar’s dividend.[2]
This has serious social implications, a report by Pew Research Centre found that during this electoral period, public confidence in mainstream news outlets had reached an unprecedented low.[50] The liar’s dividend enhances this distrust and created an environment of doubt during these elections.[51]
Cancer Myths
[edit]In the medical field, there have been several instances of false ‘cancer cure’ remedies, such as the use of sodium bicarbonate (baking soda), in spite of limited scientific evidence to confirm this.[52] Those with concerns about the side effects of traditional cancer treatments such as chemotherapy, may be more susceptible to believing in unconventional treatments.[53] This may drive individuals to choose fake remedies that appear more favourable, even if they lack scientific rigour. [54]
In a state of cognitive dissonance, where individuals already hold prior negative views of medical cancer treatments, they are more inclined to reject such treatments to avoid psychological discomfort.[54] Accepting the false claims that match their prior beliefs, creating the liar’s dividend phenomenon. These inaccurate claims increase doubt in scientific institutions, amplifying the number of people who accept the false narratives.[55]
This has serious consequences, for example increased mortality rates and delays in the commencement of effective medical treatment.[56] The liar’s dividend evident in cancer myths has negative implications that can affect one’s life, cognitively through widespread false claims and physically harm patients by delaying medical treatment.[57] However, socioeconomic status influences the refusal of conventional cancer treatments, an aspect that merits further research.
Overcoming The Impacts of The Liar’s Dividend
[edit]The liar’s dividend normalises false narratives, creating doubt in the authenticity of true evidence leading to the spread of misinformation..[1] Various psychological mechanisms drive this phenomenon such as; confirmation bias, the psychological strain that arises from cognitive dissonance and the shared in-group beliefs that create group polarisation.[3].[4]
This has severe implications in politics, where an environment of distrust, creates a space for fake news to thrive.[58] This results in circumstances such as the #BuildTheWall on twitter or risking lives through false medical claims ,as those evident in cancer myths.[59].[52].[5]
Advancing Institutional Transparency
[edit]One strategy to mitigate the liar’s dividend is advancing institutional transparency through the use of external auditing bodies that can objectively assess the evidence presented.[60] This can gradually aid the public in trusting governmental institutions, practically during electoral periods.[61]
Though, it may take time to implement this scheme on a large scale, taking the first steps can aid this process.[60] Moreover, it can increase confidence in traditional media outlets that provide sources of information, thus reducing the number of people who turn to unreliable sources.[62] As a result, minimising the effects of the liar’s divided and susceptibility to false narratives.
Algorithm Accountability
[edit]Algorithm accountability provides an avenue for individuals to widen their perspective beyond their social media feed.[63] While holding platforms responsible for the content they distribute is necessary to combat the liar's dividend, some may be resistant, as it may impact the platform's image.[64] Nonetheless, awareness of diverse content can help counteract the spread of falsehoods, therefore mitigating the impacts of the liar’s dividend.[65]
References
[edit]American Immigration Council. (2024). Debunking the myth of immigrants and crime https://www.americanimmigrationcouncil.org
Arguedas, R., Robertson, C., Fletcher, R., & Nielsen, R. (2022). Echo chambers, filter bubbles, and polarisation: A literature review. Reuters Institute for the Study of Journalism. ISBN 978-1-907384-96-7
Aßmann, L., & Betsch, T. (2023). Medical decision making beyond evidence: Correlates of belief in complementary and alternative medicine (CAM) and homeopathy. PLOS ONE, 18(4), e0284383. https://doi.org/10.1371/journal.pone.0284383
Baer, T. (Ed.). (2019). Algorithmic biases and social media. In Understand, manage, and prevent algorithmic bias: A guide for business users and data scientists (pp. 95–106). Apress. https://doi.org/10.1007/978-1-4842-4885-0_11
Banaji, M. R., Fiske, S. T., & Massey, D. S. (2021). Systemic racism: Individuals and interactions, institutions and society. Cognitive Research: Principles and Implications, 6(1), 82. https://doi.org/10.1186/s41235-021-00349-3
Beauvais, C. (2022). Fake news: Why do we believe it? Joint Bone Spine, 89(4), 105371. https://doi.org/10.1016/j.jbspin.2022.105371
Berlinski, N., Doyle, M., Guess, A. M., Levy, G., Lyons, B., Montgomery, J. M., Nyhan, B., Reifler, J. (2023). The effects of unsubstantiated claims of voter fraud on confidence in elections. Journal of Experimental Political Science, 10(1), 34–49. https://doi.org/10.1017/XPS.2021.18
Chavanayarn, S. (2024). Contextual approaches to combating fake news: Lessons from Thailand. Asian Journal of Philosophy, 3(1), 30. https://doi.org/10.1007/s44204-024-00162-x
Chesney, R., & Citron, D. (2019). Deepfakes and the new disinformation war: The coming age of post-truth geopolitics. Foreign Affairs, 98(1), 147–155.
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118
Cota, W., Ferreira, S. C., Pastor-Satorras, R., & Starnini, M. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1), 1–13. https://doi.org/10.1140/epjds/s13688-019-0213-9
De Blasio, E., & Selva, D. (2021). Who is responsible for disinformation? European approaches to social platforms' accountability in the post-truth era. American Behavioral Scientist, 65(6), 825–846. https://doi.org/10.1177/0002764221989784
Diaz Ruiz, C., & Nilsson, T. (2022). Disinformation and echo chambers: How disinformation circulates on social media through identity-driven controversies. Communication Research, 50(7), 1023–1046. https://doi.org/10.1177/07439156221103852
Eggers, A. C., Garro, H., & Grimmer, J. (2021). No evidence for systematic voter fraud: A guide to statistical claims about the 2020 election. Proceedings of the National Academy of Sciences of the United States of America, 118(45), e2103619118. https://doi.org/10.1073/pnas.2103619118
Enria, L., Dwyer, H., Marchant, M., Beckmann, N., Schmidt-Sane, M., Conteh, A., Mansaray, A., & N’Jai, A. (2024). Political dimensions of misinformation, trust, and vaccine confidence in a digital age. BMJ, 385, e079940. https://doi.org/10.1136/bmj-2024-079940
Festinger, L. (1957). A theory of cognitive dissonance. APA PsycNET.
Festinger, L., Riecken, H. W., & Schachter, S. (1956). When prophecy fails. University of Minnesota Press.
Figà Talamanca, G., & Arfini, S. (2022). Through the newsfeed glass: Rethinking filter bubbles and echo chambers. Philosophy & Technology, 35(1), 20. https://doi.org/10.1007/s13347-021-00494-z
Gaitanidis, A., Alevizakos, M., Tsalikidis, C., Tsaroucha, A., Simopoulos, C., & Pitiakoudis, M. (2018). Refusal of cancer-directed surgery by breast cancer patients: Risk factors and survival outcomes. Clinical Breast Cancer, 18(4), e469–e476. https://doi.org/10.1016/j.clbc.2017.07.010
Garett, R., & Young, S. D. (2021). Online misinformation and vaccine hesitancy. Translational Behavioral Medicine, 11(12), 2194–2199. https://doi.org/10.1093/tbm/ibab128
Gounaridis, D., & Newell, J. P. (2024). The social anatomy of climate change denial in the United States. Scientific Reports, 14(1), 2097. https://doi.org/10.1038/s41598-023-50591-6
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425), 374–378. https://doi.org/10.1126/science.aau2706
Herrero-Beaumont, E. (2023). Emerging transparency systems for news governance to protect media independence and credibility in the digital infosphere. Communication Law and Policy, 27(3-4), 220–249. https://doi.org/10.1080/10811680.2022.2154071
Holt, T. C. (2023). The Civil Rights Movement: A Very Short Introduction. Oxford University Press.
Horneber, D., & Laumer, S. (2023). Algorithmic accountability. Business & Information Systems Engineering, 65(6), 723–730. https://doi.org/10.1007/s12599-023-00817-8
Humprecht, E. (2019). How do they debunk “fake news”? A cross-national comparison of transparency in fact checks. Journalism Studies, 21(3), 310–327. https://doi.org/10.1080/21670811.2019.1691031
Hussein, E., Juneja, P., & Mitra, T. (2020). Measuring misinformation in video search platforms: An audit study on YouTube. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1), 48:1–48:27. https://doi.org/10.1145/3392854
Jimenez, T., Arndt, J., & Landau, M. J. (2021). Walls block waves: Using an inundation metaphor of immigration predicts support for a border wall. Journal of Social and Political Psychology, 9(1), 159–171. https://doi.org/10.5964/jspp.6383
Johnson, S. B., Park, H. S., Gross, C. P., & Yu, J. B. (2018). Complementary medicine, refusal of conventional cancer therapy, and survival among patients with curable cancers. JAMA Oncology, 4(10), 1375–1381. https://doi.org/10.1001/jamaoncol.2018.2487
Kaltenmeier, C., Malik, J., Yazdani, H., Geller, D. A., Medich, D., Zureikat, A., Tohme, S. (2020). Refusal of cancer-directed treatment by colon cancer patients: Risk factors and survival outcomes. The American Journal of Surgery, 220(6), 1605–1612. https://doi.org/10.1016/j.amjsurg.2020.04.022
Kanchan, S., & Gaidhane, A. (2023). Social media role and its impact on public health: A narrative review. Cureus, 15(1), e33737. https://doi.org/10.7759/cureus.33737
Kappes, A., Harvey, A. H., Lohrenz, T., Montague, P. R., & Sharot, T. (2020). Confirmation bias in the utilization of others' opinion strength. Nature Neuroscience, 23(1), 130–137. https://doi.org/10.1038/s41593-019-0549-2
Kirdemir, B., Kready, J., Mead, E., Hussain, M. N., & Agarwal, N. (2021). Examining video recommendation bias on YouTube. In L. Boratto, S. Faralli, M. Marras, & G. Stilo (Eds.), Advances in bias and fairness in information retrieval (pp. 106–116). Springer. https://doi.org/10.1007/978-3-030-78818-6_10
Knobloch-Westerwick, S., Mothes, C., & Polavin, N. (2020). Confirmation bias, ingroup bias, and negativity bias in selective exposure to political information. Communication Research, 47(1), 104–124. https://doi.org/10.1177/0093650217719596
Laidler, P. (2022). Divide and rule: Political impact of President Trump's US-Mexico border wall initiative. Politeja, 19(6[81]), 253–278. https://doi.org/10.12797/Politeja.19.2022.81.13
Lange, R. D., Chattoraj, A., Beck, J. M., Yates, J. L., & Haefner, R. M. (2021). A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLOS Computational Biology, 17(11), e1009517. https://doi.org/10.1371/journal.pcbi.1009517
Leary, J. P. (2017). Decoding “Build the Wall”: What liberal critics miss. Critical Studies in Media Communication, 34(3), 146–148. https://doi.org/10.1080/10714839.2017.1331808
Light, M. T., He, J., & Robey, J. P. (2020). Comparing crime rates between undocumented immigrants, legal immigrants, and native-born US citizens in Texas. Proceedings of the National Academy of Sciences, 117(51), 32340–32347. https://doi.org/10.1073/pnas.2014704117
Lu, J., Sun, M., & Liu, Z. (2024). Social media and political polarization: A panel study of 36 countries from 2014 to 2020. Social Indicators Research. https://doi.org/10.1007/s11205-024-03367-y
Mohammed, S. N. (2019). Conspiracy theories and flat-earth videos on YouTube. The Journal of Social Media in Society, 8(2), 84–102. https://thejsms.org/index.php/JSMS/article/view/527
Müller, J., Tellier, A., & Kurschilgen, M. (2022). Echo chambers and opinion dynamics explain the occurrence of vaccination hesitancy. Royal Society Open Science, 9(10), 220367. https://doi.org/10.1098/rsos.220367
Nadarevic, L., Reber, R., Helmecke, A. J., & Köse, D. (2020). Perceived truth of statements and simulated social media postings: An experimental investigation of source credibility, repeated exposure, and presentation format. Cognitive Research: Principles and Implications, 5(1), 56. https://doi.org/10.1186/s41235-020-00251-4
Newman, M. (2018). Is cancer fundraising fuelling quackery? BMJ, 362, k3829. https://doi.org/10.1136/bmj.k3829
Ognyanova, K., Lazer, D., Robertson, R. E., & Wilson, C. (2020). Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-024
Ouyang, Y., & Waterman, R. W. (2020). Trump, Twitter, and the American democracy. In Y. Ouyang & R. W. Waterman (Eds.), Trump, Twitter, and the American democracy: Political communication in the digital age (pp. 131–161). Springer. https://doi.org/10.1007/978-3-030-44242-2_5
Palmucci, D. N., & Ferraris, A. (2023). Climate change inaction: Cognitive bias influencing managers' decision-making on environmental sustainability choices. The role of empathy and morality with the need for an integrated and comprehensive perspective. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1130059
Pawelec, M. (2022). Deepfakes and democracy (theory): How synthetic audio-visual media for disinformation and hate speech threaten core democratic functions. Digital Society, 1(2), 19. https://doi.org/10.1007/s44206-022-00010-6
Pecile, G., Di Marco, N., Cinelli, M., & Quattrociocchi, W. (2024). Decoding political polarization in social media interactions. https://doi.org/10.485
Pennycook, G., & Rand, D. G. (2021). Research note: Examining false beliefs about voter fraud in the wake of the 2020 Presidential Election. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-51
Pew Research Center. (2016). Confidence in election, views of U.S. democracy. https://www.pewresearch.org
Piksa, M., Noworyta, K., Gundersen, A., Kunst, J., Morzy, M., Piasecki, J., & Rygula, R. (2024). The impact of confirmation bias awareness on mitigating susceptibility to misinformation. Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1414864
ProQuest. (2024). The factors behind the fake news label: Why some people distrust news media. https://www.proquest.com
Robert, C., & Chesney, R. (2018). Deep fakes: A looming challenge for privacy, democracy, and national security. https://doi.org/10.2139/doi
Russell-Brown, K. (2018). The academic swoon over implicit racial bias: Costs, benefits, and other considerations. Du Bois Review: Social Science Research on Race, 15(1), 185–193. https://doi.org/10.1017/S1742058X18000073
Ryan, P. (2022). The accountability of algorithms on social media platforms. In Regulatory insights on artificial intelligence (pp. 240–261). Edward Elgar Publishing. https://doi.org/10.4337/9781800880788.00019
Santamaría Graff, C. C. (2017). ‘Build that wall!’: Manufacturing the enemy, yet again. Journal of Latin American Cultural Studies, 26(6), 999–1005. https://doi.org/10.1080/09518398.2017.1312592
Sieber, J., & Ziegler, R. (2019). Group polarization revisited: A processing effort account. Personality and Social Psychology Bulletin, 45(10), 1482–1498. https://doi.org/10.1177/0146167219833389
Sindermann, C., Schmitt, H. S., Rozgonjuk, D., Elhai, J. D., & Montag, C. (2021). The evaluation of fake and true news: On the role of intelligence, personality, interpersonal trust, ideological attitudes, and news consumption. Heliyon, 7(3), e06503. https://doi.org/10.1016/j.heliyon.2021.e06503
Suzuki, M., & Yamamoto, Y. (2021). Characterizing the influence of confirmation bias on web search behavior. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.771948
Törnberg, P. (2018). Echo chambers and viral misinformation: Modeling fake news as complex contagion. PLOS ONE, 13(9), e0203958. https://doi.org/10.1371/journal.pone.0203958
West, J. D., & Bergstrom, C. T. (2021). Misinformation in and about science. Proceedings of the National Academy of Sciences, 118(15), e1912444117. https://doi.org/10.1073/pnas.1912444117
Yang, M., Zhong, X., & Yuan, Y. (2020). Does baking soda function as a magic bullet for patients with cancer? A mini review. Integrative Cancer Therapies, 19, 1534735420922579. https://doi.org/10.1177/1534735420922579
Zimmermann, D., Noll, C., Gräßer, L., Hugger, K.-U., Braun, L. M., Nowak, T., & Kaspar, K. (2022). Influencers on YouTube: A quantitative study on young people's use and perception of videos about political and societal topics. Current Psychology, 41(10), 6808–6824. https://doi.org/10.1007/s12144-020-01164-7
- ^ a b c d e Robert, Chesney,; Keats, Citron, Danielle (2018-07-14). "Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security". doi:10.2139/.
{{cite journal}}
: Check|doi=
value (help); Cite journal requires|journal=
(help)CS1 maint: extra punctuation (link) CS1 maint: multiple names: authors list (link) - ^ a b c d e "OSF". osf.io. Retrieved 2024-12-11.
- ^ a b Knobloch-Westerwick, Silvia; Mothes, Cornelia; Polavin, Nick (2020-02-01). "Confirmation Bias, Ingroup Bias, and Negativity Bias in Selective Exposure to Political Information". Communication Research. 47 (1): 104–124. doi:10.1177/0093650217719596. ISSN 0093-6502.
- ^ a b c d Leon, Festinger, (1957). "A theory of cognitive dissonance". APA PsycNET.
{{cite journal}}
: CS1 maint: extra punctuation (link) CS1 maint: multiple names: authors list (link) - ^ a b c Sieber, Janusch; Ziegler, René (2019-10-01). "Group Polarization Revisited: A Processing Effort Account". Personality and Social Psychology Bulletin. 45 (10): 1482–1498. doi:10.1177/0146167219833389. ISSN 0146-1672. PMC 6732819. PMID 30885061.
{{cite journal}}
: CS1 maint: PMC format (link) - ^ Pawelec, Maria (2022-09-08). "Deepfakes and Democracy (Theory): How Synthetic Audio-Visual Media for Disinformation and Hate Speech Threaten Core Democratic Functions". Digital Society. 1 (2): 19. doi:10.1007/s44206-022-00010-6. ISSN 2731-4669. PMC 9453721. PMID 36097613.
{{cite journal}}
: CS1 maint: PMC format (link) - ^ Knobloch-Westerwick, Silvia; Mothes, Cornelia; Polavin, Nick (2020-02-01). "Confirmation Bias, Ingroup Bias, and Negativity Bias in Selective Exposure to Political Information". Communication Research. 47 (1): 104–124. doi:10.1177/0093650217719596. ISSN 0093-6502.
- ^ Lange, Richard D.; Chattoraj, Ankani; Beck, Jeffrey M.; Yates, Jacob L.; Haefner, Ralf M. (2021-11-29). "A confirmation bias in perceptual decision-making due to hierarchical approximate inference". PLOS Computational Biology. 17 (11): e1009517. doi:10.1371/journal.pcbi.1009517. ISSN 1553-7358. PMC 8659691. PMID 34843452.
{{cite journal}}
: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link) - ^ Suzuki, Masaki; Yamamoto, Yusuke (2021-12-06). "Characterizing the Influence of Confirmation Bias on Web Search Behavior". Frontiers in Psychology. 12. doi:10.3389/fpsyg.2021.771948. ISSN 1664-1078.
{{cite journal}}
: CS1 maint: unflagged free DOI (link) - ^ Piksa, Michal; Noworyta, Karolina; Gundersen, Aleksander; Kunst, Jonas; Morzy, Mikolaj; Piasecki, Jan; Rygula, Rafal (2024-10-15). "The impact of confirmation bias awareness on mitigating susceptibility to misinformation". Frontiers in Public Health. 12. doi:10.3389/fpubh.2024.1414864. ISSN 2296-2565.
{{cite journal}}
: CS1 maint: unflagged free DOI (link) - ^ Beauvais, Catherine (2022-07-01). "Fake news: Why do we believe it?". Joint Bone Spine. 89 (4): 105371. doi:10.1016/j.jbspin.2022.105371. ISSN 1297-319X.
- ^ Thomas C., Holt (23 February 2023). The Civil Rights Movement: A Very Short Introduction. Oxford University Press.
{{cite book}}
: CS1 maint: date and year (link) - ^ Russell-Brown, Katheryn (2018-04). "THE ACADEMIC SWOON OVER IMPLICIT RACIAL BIAS: Costs, Benefits, and Other Considerations". Du Bois Review: Social Science Research on Race. 15 (1): 185–193. doi:10.1017/S1742058X18000073. ISSN 1742-058X.
{{cite journal}}
: Check date values in:|date=
(help) - ^ Banaji, Mahzarin R.; Fiske, Susan T.; Massey, Douglas S. (2021-12-20). "Systemic racism: individuals and interactions, institutions and society". Cognitive Research: Principles and Implications. 6 (1): 82. doi:10.1186/s41235-021-00349-3. ISSN 2365-7464. PMC 8688641. PMID 34931287.
{{cite journal}}
: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link) - ^ a b www.apa.org https://www.apa.org/news/press/releases/2022/05/denial-structural-racism-antiblack-prejudice?utm_source=chatgpt.com. Retrieved 2024-12-11.
{{cite web}}
: Missing or empty|title=
(help) - ^ Gounaridis, Dimitrios; Newell, Joshua P. (2024-02-14). "The social anatomy of climate change denial in the United States". Scientific Reports. 14 (1): 2097. doi:10.1038/s41598-023-50591-6. ISSN 2045-2322.
- ^ Palmucci, Dario Natale; Ferraris, Alberto (2023-03-06). "Climate change inaction: Cognitive bias influencing managers' decision making on environmental sustainability choices. The role of empathy and morality with the need of an integrated and comprehensive perspective". Frontiers in Psychology. 14. doi:10.3389/fpsyg.2023.1130059. ISSN 1664-1078.
{{cite journal}}
: CS1 maint: unflagged free DOI (link) - ^ Festinger, Leon; Riecken, Henry W.; Schachter, Stanley (1956). When prophecy fails. Minneapolis: University of Minnesota Press. doi:10.1037/10030-000.
- ^ a b Cinelli, Matteo; De Francisci Morales, Gianmarco; Galeazzi, Alessandro; Quattrociocchi, Walter; Starnini, Michele (2021-03-02). "The echo chamber effect on social media". Proceedings of the National Academy of Sciences. 118 (9): e2023301118. doi:10.1073/pnas.2023301118. PMC 7936330. PMID 33622786.
{{cite journal}}
: CS1 maint: PMC format (link) - ^ Ross Arguedas, A.; Robertson, C.; Fletcher, R.; Nielsen, R. (2022). Echo chambers, filter bubbles, and polarisation: a literature review. Reuters Institute for the Study of Journalism. ISBN 978-1-907384-96-7.
- ^ a b Törnberg, Petter (2018-09-20). "Echo chambers and viral misinformation: Modeling fake news as complex contagion". PLOS ONE. 13 (9): e0203958. doi:10.1371/journal.pone.0203958. ISSN 1932-6203. PMC 6147442. PMID 30235239.
{{cite journal}}
: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link) - ^ Müller, Johannes; Tellier, Aurélien; Kurschilgen, Michael (2022-10-12). "Echo chambers and opinion dynamics explain the occurrence of vaccination hesitancy". Royal Society Open Science. 9 (10): 220367. doi:10.1098/rsos.220367. PMC 9554521. PMID 36312563.
{{cite journal}}
: CS1 maint: PMC format (link) - ^ Piksa, Michal; Noworyta, Karolina; Gundersen, Aleksander; Kunst, Jonas; Morzy, Mikolaj; Piasecki, Jan; Rygula, Rafal (2024-10-15). "The impact of confirmation bias awareness on mitigating susceptibility to misinformation". Frontiers in Public Health. 12. doi:10.3389/fpubh.2024.1414864. ISSN 2296-2565.
{{cite journal}}
: CS1 maint: unflagged free DOI (link) - ^ Cota, Wesley; Ferreira, Silvio C.; Pastor-Satorras, Romualdo; Starnini, Michele (2019-12). "Quantifying echo chamber effects in information spreading over political communication networks". EPJ Data Science. 8 (1): 1–13. doi:10.1140/epjds/s13688-019-0213-9. ISSN 2193-1127.
{{cite journal}}
: Check date values in:|date=
(help) - ^ Enria, Luisa; Dwyer, Harriet; Marchant, Mark; Beckmann, Nadine; Schmidt-Sane, Megan; Conteh, Abu; Mansaray, Anthony; N’Jai, Alhaji (2024-06-20). "Political dimensions of misinformation, trust, and vaccine confidence in a digital age". BMJ. 385: e079940. doi:10.1136/bmj-2024-079940. ISSN 1756-1833. PMID 38901859.
- ^ Garett, Renee; Young, Sean D. (2021-12-14). "Online misinformation and vaccine hesitancy". Translational Behavioral Medicine. 11 (12): 2194–2199. doi:10.1093/tbm/ibab128. ISSN 1613-9860. PMC 8515268. PMID 34529080.
- ^ "Internet and social media users in the world 2024". Statista. Retrieved 2024-12-11.
- ^ Kanchan, Sushim; Gaidhane, Abhay (2023-01). "Social Media Role and Its Impact on Public Health: A Narrative Review". Cureus. 15 (1): e33737. doi:10.7759/cureus.33737. ISSN 2168-8184. PMC 9925030. PMID 36793805.
{{cite journal}}
: Check date values in:|date=
(help)CS1 maint: unflagged free DOI (link) - ^ Chesney, Robert; Citron, Danielle (2019). "Deepfakes and the New Disinformation War: The Coming Age of Post-Truth Geopolitics". Foreign Affairs. 98 (1): 147–155. ISSN 0015-7120.
- ^ Baer, Tobias (2019), Baer, Tobias (ed.), "Algorithmic Biases and Social Media", Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists, Berkeley, CA: Apress, pp. 95–106, doi:10.1007/978-1-4842-4885-0_11?utm_source=chatgpt.com, ISBN 978-1-4842-4885-0, retrieved 2024-12-11
- ^ Nadarevic, Lena; Reber, Rolf; Helmecke, Anne Josephine; Köse, Dilara (2020-11-11). "Perceived truth of statements and simulated social media postings: an experimental investigation of source credibility, repeated exposure, and presentation format". Cognitive Research: Principles and Implications. 5 (1): 56. doi:10.1186/s41235-020-00251-4. ISSN 2365-7464. PMC 7656226. PMID 33175284.
{{cite journal}}
: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link) - ^ Leary, John Patrick (2017-04-03). doi:10.1080/10714839.2017.1331808?casa_token=5wkrwgxtutgaaaaa:uadme6lkkl1-e0ztht2xluw1uc4xyummmjzmk4u9d5afssft9rloevmml-4b3ibqbz4yynj9hg&casa_token=thggpfqxh_4aaaaa:tap1usqrciv8gh9mik6cnjzcivqrd--fznyn2fnamuzxrctngtbhik3aa1nbrwizbhm_nqxbdw. ISSN 1071-4839 https://www.tandfonline.com/doi/full/10.1080/10714839.2017.1331808?casa_token=5WkRwGxTUtgAAAAA%3AuAdmE6LKKl1-E0ZTHt2XlUW1UC4XYUMMmJZMK4U9D5AfssFt9RlOEVmmL-4b3IBQbz4YYNJ9Hg&casa_token=thGGPfqxH_4AAAAA%3ATaP1USQRCIV8Gh9MiK6CnjzcivQrd--FznYN2FnamUzxRCtnGtbhiK3aA1NBrWizBhm_NqXBdw&.
{{cite journal}}
: Cite journal requires|journal=
(help); Missing or empty|title=
(help) - ^ Santamaría Graff, Cristina C. (2017-11-26). doi:10.1080/09518398.2017.1312592?casa_token=r7jfugzve5iaaaaa:z3iwmuyfmxjwbae62cct1rvsutvhn1dsgwee5akkmjgrfq6wqkiovnpofwosmtbq5ywmnyh8mw. ISSN 0951-8398 https://www.tandfonline.com/doi/full/10.1080/09518398.2017.1312592?casa_token=R7JfuGzve5IAAAAA%3Az3IWMuyfmxjWBAe62cCT1rvsUtVHN1dsGWEE5akKMJGrFQ6WQkiovnPoFwosmtBQ5YWMNyH8Mw&.
{{cite journal}}
: Cite journal requires|journal=
(help); Missing or empty|title=
(help) - ^ Laidler, Paweł (2022-12-28). "Divide and Rule: Political Impact of President Trump's US-Mexico Border Wall Initiative". Politeja. 19 (6(81)): 253–278. doi:10.12797/Politeja.19.2022.81.13. ISSN 2391-6737.
- ^ a b Pecile, Giulio; Marco, Niccolò Di; Cinelli, Matteo; Quattrociocchi, Walter (2024-07-04), Decoding Political Polarization in Social Media Interactions, doi:10.48550/arXiv.2407.03773, retrieved 2024-12-11
- ^ Lu, Jia; Sun, Meiqi; Liu, Zikun (2024-06-06). "Social Media and Political Polarization: A Panel Study of 36 Countries from 2014 to 2020". Social Indicators Research. doi:10.1007/s11205-024-03367-y. ISSN 1573-0921.
- ^ Light, Michael T.; He, Jingying; Robey, Jason P. (2020-12-22). "Comparing crime rates between undocumented immigrants, legal immigrants, and native-born US citizens in Texas". Proceedings of the National Academy of Sciences. 117 (51): 32340–32347. doi:10.1073/pnas.2014704117. PMC 7768760. PMID 33288713.
{{cite journal}}
: CS1 maint: PMC format (link) - ^ "Debunking the Myth of Immigrants and Crime". American Immigration Council. 2024-10-17. Retrieved 2024-12-11.
- ^ Zimmermann, Daniel; Noll, Christian; Gräßer, Lars; Hugger, Kai-Uwe; Braun, Lea Marie; Nowak, Tine; Kaspar, Kai (2022-10-01). "Influencers on YouTube: a quantitative study on young people's use and perception of videos about political and societal topics". Current Psychology. 41 (10): 6808–6824. doi:10.1007/s12144-020-01164-7. ISSN 1936-4733.
- ^ Mohammed, Shaheed N. (2019-12-31). "Conspiracy Theories and Flat-Earth Videos on YouTube". The Journal of Social Media in Society. 8 (2): 84–102. ISSN 2325-503X.
- ^ "Disinformation and Echo Chambers: How Disinformation Circulates on Social Media Through Identity-Driven Controversies". journals.sagepub.com. doi:10.1177/07439156221103852?utm_source=chatgpt.com. Retrieved 2024-12-11.
- ^ Figà Talamanca, Giacomo; Arfini, Selene (2022-03-15). "Through the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers". Philosophy & Technology. 35 (1): 20. doi:10.1007/s13347-021-00494-z. ISSN 2210-5441. PMC 8923337. PMID 35308101.
{{cite journal}}
: no-break space character in|title=
at position 54 (help)CS1 maint: PMC format (link) - ^ Kirdemir, Baris; Kready, Joseph; Mead, Esther; Hussain, Muhammad Nihal; Agarwal, Nitin (2021). Boratto, Ludovico; Faralli, Stefano; Marras, Mirko; Stilo, Giovanni (eds.). "Examining Video Recommendation Bias on YouTube". Advances in Bias and Fairness in Information Retrieval. Cham: Springer International Publishing: 106–116. doi:10.1007/978-3-030-78818-6_10. ISBN 978-3-030-78818-6.
- ^ Hussein, Eslam; Juneja, Prerna; Mitra, Tanushree (2020-05-29). "Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube". Proc. ACM Hum.-Comput. Interact. 4 (CSCW1): 48:1–48:27. doi:10.1145/3392854.
- ^ Sindermann, Cornelia; Schmitt, Helena Sophia; Rozgonjuk, Dmitri; Elhai, Jon D.; Montag, Christian (2021-03-01). "The evaluation of fake and true news: on the role of intelligence, personality, interpersonal trust, ideological attitudes, and news consumption". Heliyon. 7 (3): e06503. doi:10.1016/j.heliyon.2021.e06503. ISSN 2405-8440.
{{cite journal}}
: CS1 maint: unflagged free DOI (link) - ^ Grinberg, Nir; Joseph, Kenneth; Friedland, Lisa; Swire-Thompson, Briony; Lazer, David (2019-01-25). "Fake news on Twitter during the 2016 U.S. presidential election". Science (New York, N.Y.). 363 (6425): 374–378. doi:10.1126/science.aau2706. ISSN 1095-9203. PMID 30679368.
- ^ Ouyang, Yu; Waterman, Richard W. (2020), Ouyang, Yu; Waterman, Richard W. (eds.), "Trump, Twitter, and the American Democracy", Trump, Twitter, and the American Democracy: Political Communication in the Digital Age, Cham: Springer International Publishing, pp. 131–161, doi:10.1007/978-3-030-44242-2_5, ISBN 978-3-030-44242-2, retrieved 2024-12-12
- ^ Eggers, Andrew C.; Garro, Haritz; Grimmer, Justin (2021-11-09). "No evidence for systematic voter fraud: A guide to statistical claims about the 2020 election". Proceedings of the National Academy of Sciences of the United States of America. 118 (45): e2103619118. doi:10.1073/pnas.2103619118. ISSN 1091-6490. PMC 8609310. PMID 34728563.
- ^ Kappes, Andreas; Harvey, Ann H.; Lohrenz, Terry; Montague, P. Read; Sharot, Tali (2020-01). "Confirmation bias in the utilization of others' opinion strength". Nature Neuroscience. 23 (1): 130–137. doi:10.1038/s41593-019-0549-2. ISSN 1546-1726. PMID 31844311.
{{cite journal}}
: Check date values in:|date=
(help) - ^ "5. Confidence in election, views of U.S. democracy". Pew Research Center. 2016-10-27. Retrieved 2024-12-12.
- ^ Ognyanova, Katherine; Lazer, David; Robertson, Ronald E.; Wilson, Christo (2020-06-02). "Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power". Harvard Kennedy School Misinformation Review. doi:10.37016/mr-2020-024.
- ^ a b Yang, Mengyuan; Zhong, Xian; Yuan, Ying (2020-01-01). "Does Baking Soda Function as a Magic Bullet for Patients With Cancer? A Mini Review". Integrative Cancer Therapies. 19: 1534735420922579. doi:10.1177/1534735420922579. ISSN 1534-7354. PMC 7249593. PMID 32448009.
{{cite journal}}
: CS1 maint: PMC format (link) - ^ Newman, Melanie (2018-09-12). "Is cancer fundraising fuelling quackery?". BMJ. 362: k3829. doi:10.1136/bmj.k3829. ISSN 0959-8138. PMID 30209055.
- ^ a b Aßmann, Leonie; Betsch, Tilmann (2023-04-21). "Medical decision making beyond evidence: Correlates of belief in complementary and alternative medicine (CAM) and homeopathy". PLOS ONE. 18 (4): e0284383. doi:10.1371/journal.pone.0284383. ISSN 1932-6203.
{{cite journal}}
: CS1 maint: unflagged free DOI (link) - ^ West, Jevin D.; Bergstrom, Carl T. (2021-04-13). "Misinformation in and about science". Proceedings of the National Academy of Sciences. 118 (15): e1912444117. doi:10.1073/pnas.1912444117. PMC 8054004. PMID 33837146.
{{cite journal}}
: CS1 maint: PMC format (link) - ^ Johnson, Skyler B.; Park, Henry S.; Gross, Cary P.; Yu, James B. (2018-10-01). "Complementary Medicine, Refusal of Conventional Cancer Therapy, and Survival Among Patients With Curable Cancers". JAMA oncology. 4 (10): 1375–1381. doi:10.1001/jamaoncol.2018.2487. ISSN 2374-2445. PMC 6233773. PMID 30027204.
- ^ Gaitanidis, Apostolos; Alevizakos, Michail; Tsalikidis, Christos; Tsaroucha, Alexandra; Simopoulos, Constantinos; Pitiakoudis, Michail (2018-08-01). "Refusal of Cancer-Directed Surgery by Breast Cancer Patients: Risk Factors and Survival Outcomes". Clinical Breast Cancer. 18 (4): e469–e476. doi:10.1016/j.clbc.2017.07.010. ISSN 1526-8209.
- ^ "The Factors behind the Fake News Label: Why Some People Distrust News Media - ProQuest". www.proquest.com. Retrieved 2024-12-12.
- ^ Jimenez, Tyler; Arndt, Jamie; Landau, Mark J. (2021-04-20). "Walls Block Waves: Using an Inundation Metaphor of Immigration Predicts Support for a Border Wall". Journal of Social and Political Psychology. 9 (1): 159–171. doi:10.5964/jspp.6383. ISSN 2195-3325.
- ^ a b Humprecht, Edda (2020-03-15). doi:10.1080/21670811.2019.1691031?casa_token=ainiols_r44aaaaa:z3jatm0ouzuh-ijwrhkm7c8atbcoyrl2p5ddz4l2ktxnauqacvhmiqvidpyhd34rekxct8pd8q. ISSN 2167-0811 https://www.tandfonline.com/doi/full/10.1080/21670811.2019.1691031?casa_token=AIniols_R44AAAAA%3Az3JatM0oUZUh-ijWrHkm7C8ATbcoyRL2p5ddz4L2KtxNAuQAcvHmIQvIdPyhD34rEKXCT8pD8Q&.
{{cite journal}}
: Cite journal requires|journal=
(help); Missing or empty|title=
(help) - ^ Chavanayarn, Siraprapa (2024-05-01). "Contextual approaches to combating fake news: lessons from Thailand". Asian Journal of Philosophy. 3 (1): 30. doi:10.1007/s44204-024-00162-x. ISSN 2731-4642.
- ^ Herrero-Beaumont, Elena (2022-10-02). doi:10.1080/10811680.2022.2154071?casa_token=vsk9qpykidiaaaaa:0gcq-0xy9e9skjnk9bdm6itanbwc2i63zitlhkcpl5ydl_mkynn0kebudpyfpqwkffsasq_isa#abstract. ISSN 1081-1680 https://www.tandfonline.com/doi/full/10.1080/10811680.2022.2154071?casa_token=VSk9qpyKIdIAAAAA%3A0GCq-0xY9E9sKjNK9Bdm6ItaNbwC2i63zITLHkcPL5ydL_mKYnn0KEbuDPYfPQwKFFsasq_IsA&.
{{cite journal}}
: Cite journal requires|journal=
(help); Missing or empty|title=
(help) - ^ Horneber, David; Laumer, Sven (2023-12-01). "Algorithmic Accountability". Business & Information Systems Engineering. 65 (6): 723–730. doi:10.1007/s12599-023-00817-8. ISSN 1867-0202.
- ^ De Blasio, Emiliana; Selva, Donatella (2021-05-01). "Who Is Responsible for Disinformation? European Approaches to Social Platforms' Accountability in the Post-Truth Era". American Behavioral Scientist. 65 (6): 825–846. doi:10.1177/0002764221989784. ISSN 0002-7642.
- ^ Ryan, Philippa (2022-06-10), "The accountability of algorithms on social media platforms", Regulatory Insights on Artificial Intelligence, Edward Elgar Publishing, pp. 240–261, ISBN 978-1-80088-078-8, retrieved 2024-12-12