False Stories Research Articles

How reliance on Spanish-language social media predicts beliefs in false political narratives amongst Latinos.

False political narratives are nearly inescapable on social media in the United States. They are a particularly acute problem for Latinos, and especially for those who rely on Spanish-language social media for news and information. Studies have shown that Latinos are vulnerable to misinformation because they rely more heavily on social media and messaging platforms than non-Hispanic whites. Moreover, fact-checking algorithms are not as robust in Spanish as they are in English, and social media platforms put far more effort into combating misinformation on English-language media than Spanish-language media, which compounds the likelihood of being exposed to misinformation. As a result, we expect that Latinos who use Spanish-language social media to be more likely to believe in false political narratives when compared with Latinos who primarily rely on English-language social media for news. To test this expectation, we fielded the largest online survey to date of social media usage and belief in political misinformation of Latinos. Our study, fielded in the months leading up to and following the 2022 midterm elections, examines a variety of false political narratives that were circulating in both Spanish and English on social media. We find that social media reliance for news predicts one's belief in false political stories, and that Latinos who use Spanish-language social media have a higher probability of believing in false political narratives, compared with Latinos using English-language social media.

Abuse of Criticism: T.S. Eliot and Northrop Frye’s Academic Ramp-up to “Fake News”

Percy Bysshe Shelley’s “Defence of Poetry” originates T.S. Eliot’s 1932 Harvard Norton Lecture on “taste” maligning “a marked swing in his feelings toward Shelley's verse from fervid imitation to contemptuous disdain.” Lowe, P.J., 2002. Eliot’s acolyte, Northrop Frye, in 1957 follows similar tautology to exclude Shelley’s straw-man agonist, Thomas Love Peacock, from Anatomy of Criticism commentary over Sir Philip Sidney’s “Apology for Poesy,” Shelley’s obvious rhetorical target. Here we treat Eliot and Frye’s methodologies as a Prigozhin troll factory authoritarian oligarchic academic coup that, “like the Internet Research Agency, engage in … ‘coordinated inauthentic activity, ranging from the use of false names and the creation of false audiences to the publication of false stories and the creation of divisive narratives.”

Israiliyyat Prophet Sulaiman In Tafsir Bi Al-Ma'thur: A Study Of Tafsir Al-Tashil Li 'Ulum Al-Tanzil By Ibn Juzay Al-Kalbi

This study aims to explore the existence of israiliyyat contained in Tafsir al-Tashil li 'Ulum al-Tanzil by examining the verses of the story of Prophet Sulaiman in the Quran. The source of israiliyyat is essential to study in order to avoid deviant understandings that can damage the order of the creed of Islam and emphasize the interpretations that eliminate the kema'shuman of the Prophets. This research uses a qualitative method with a literature study. Data were obtained through an interpretive method using the hermeneutic approach to understand the phenomenon of shifting meaning and semiotics to get the most optimal meaning. The results showed that the story of the Prophet Sulaiman was classified into five themes, with four themes considered as the category that provide information only, and one theme was considered as the category of israiliyyat, namely the interpretation in Surah Shad verse 34 by providing a rebuttal to the false story and cannot be used as a reference for interpretation. Ibn Juzay also quotes some israiliyyat stories without criticism and judgment as to whether to reject or accept their narration, which can be called tawaqquf. Nevertheless, Ibn Juzay's interpretation does not contradict the Qur'an and the Hadith, does not damage the order of Islamic religious principles, and does not eliminate the humanity of the Prophet Sulaiman

Post-Truth American Politics: False Stories and Current Crises by David Ricci

Post-Truth American Politics: False Stories and Current Crises by <i>David Ricci</i>

The strength of weak ties and fake news believability

Are we more likely to believe a social media news story shared by someone with whom we have a strong or weak tie? We tend to trust close ties more than weak ties, but weak ties are sources of new information more often than strong ones. We conducted an online experiment to examine the effect of tie strength (strong ties vs. weak ties) on the decision to believe or not believe fake news stories. Participants perceived false stories from weak ties to be more believable than false stories from strong ties (after controlling for the trustworthiness of the sharer). We found that a sharer's perceived ability to share reliable information plays a significant role in individuals' decision to believe news stories on social media, regardless of whether the source is a strong or weak tie. Interestingly, a sharer's perceived integrity was found to be important only when the information came from weak ties, while a sharer's perceived benevolence was not important for either weak or strong ties. These findings show that the perceived integrity of the sharer is a key factor in the decision to believe stories from weak ties, more so than from strong ties. Furthermore, a sharer's perceived ability to share reliable information is less critical when weak ties share true stories. The impact of weak ties does not stem from the novelty of their information, as we used identical headlines across both study groups. Thus, while the strength of weak ties effect is present in this context, the underlying theoretical mechanism differs from the novelty of information traditionally observed in other settings.

Us versus them: The role of national identity in the formation of false memories for fake news.

People are prone to forming false memories for fictitious events described in fake news stories. In this preregistered study, we hypothesized that the formation of false memories may be promoted when the fake news includes stereotypes that reflect positively on one's own nationality or negatively on another nationality. We exposed German and Irish participants (N = 1,184) to fabricated news stories that were consistent with positive or negative stereotypes about Germany and Ireland. The predicted three-way interaction was not observed. Exploratory follow-up analyses revealed the expected pattern of results for German participants but not for Irish participants, who were more likely to remember positive stories and stories about Ireland. Individual differences in patriotism did not significantly affect false memory rates; however, higher levels of cognitive ability and analytical reasoning decreased false memories and increased participants' ability to distinguish between true and false news stories. These results demonstrate that stereotypical information pertaining to national identity can influence the formation of false memories for fake news, but variations in cultural context may affect how misinformation is received and processed. We conclude by urging researchers to consider the sociopolitical and media landscape when predicting the consequences of fake news exposure. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The distorting effects of producer strategies: Why engagement does not reveal consumer preferences for misinformation

A great deal of empirical research has examined who falls for misinformation and why. Here, we introduce a formal game-theoretic model of engagement with news stories that captures the strategic interplay between (mis)information consumers and producers. A key insight from the model is that observed patterns of engagement do not necessarily reflect the preferences of consumers. This is because producers seeking to promote misinformation can use strategies that lead moderately inattentive readers to engage more with false stories than true ones-even when readers prefer more accurate over less accurate information. We then empirically test people's preferences for accuracy in the news. In three studies, we find that people strongly prefer to click and share news they perceive as more accurate-both in a general population sample, and in a sample of users recruited through Twitter who had actually shared links to misinformation sites online. Despite this preference for accurate news-and consistent with the predictions of our model-we find markedly different engagement patterns for articles from misinformation versus mainstream news sites. Using 1,000 headlines from 20 misinformation and 20 mainstream news sites, we compare Facebook engagement data with 20,000 accuracy ratings collected in a survey experiment. Engagement with a headline is negatively correlated with perceived accuracy for misinformation sites, but positively correlated with perceived accuracy for mainstream sites. Taken together, these theoretical and empirical results suggest that consumer preferences cannot be straightforwardly inferred from empirical patterns of engagement.

The adaptive community-response (ACR) method for collecting misinformation on social media

Social media can be a major accelerator of the spread of misinformation, thereby potentially compromising both individual well-being and social cohesion. Despite significant recent advances, the study of online misinformation is a relatively young field facing several (methodological) challenges. In this regard, the detection of online misinformation has proven difficult, as online large-scale data streams require (semi-)automated, highly specific and therefore sophisticated methods to separate posts containing misinformation from irrelevant posts. In the present paper, we introduce the adaptive community-response (ACR) method, an unsupervised technique for the large-scale collection of misinformation on Twitter (now known as ’X’). The ACR method is based on previous findings showing that Twitter users occasionally reply to misinformation with fact-checking by referring to specific fact-checking sites (crowdsourced fact-checking). In a first step, we captured such misinforming but fact-checked tweets. These tweets were used in a second step to extract specific linguistic features (keywords), enabling us to collect also those misinforming tweets that were not fact-checked at all as a third step. We initially present a mathematical framework of our method, followed by an explicit algorithmic implementation. We then evaluate ACR on the basis of a comprehensive dataset consisting of >25\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>25$$\\end{document} million tweets, belonging to >300\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>300$$\\end{document} misinforming stories. Our evaluation shows that ACR is a useful extension to the methods pool of the field, enabling researchers to collect online misinformation more comprehensively. Text similarity measures clearly indicated correspondence between the claims of false stories and the ACR tweets, even though ACR performance was heterogeneously distributed across the stories. A baseline comparison to the fact-checked tweets showed that the ACR method can detect story-related tweets to a comparable degree, while being sensitive to different types of tweets: Fact-checked tweets tend to be driven by high outreach (as indicated by a high number of retweets), whereas the sensitivity of the ACR method extends to tweets exhibiting lower outreach. Taken together, ACR’s capacity as a valuable methodological contribution to the field is based on (i) the adoption of prior, pioneering research in the field, (ii) a well-formalized mathematical framework and (iii) an empirical foundation via a comprehensive set of indicators.

Utjecaj medija i vijesti o bolesti COVID-19 na burze i kripto tržišta

The entire world was significantly impacted by the COVID-19 pandemic,leading to confusion and turbulence in healthcare systems, as well as toextreme uncertainty in the financial realm. There was a profound distressin stock markets due to the enormously elevated levels of risk instigated bythe pandemic, which additionally inflated investors’ losses. The contagionwas supplemented by a surge of false stories that appear to be news (“fakenews”) that circulated throughout all media channels, thus additionally amplifyinguncertainty. This paper expands the previous literature by investigatingmedia impact on prominent indices of the stock and cryptocurrencymarkets. RavenPack Panic and Fake News indices were utilized to examinetheir influence on S&amp;amp;P 500 and crypto (Royalton CRIX) indices’ dailymovements by employing OLS and quantile regression. The results exhibitthat there are noteworthy dependencies over the conditional distribution,but they present only a small influence on observed indices. Fake news influencesthe cryptocurrencies’ daily movements in a bearish market, howeverthe effect is very weak. Furthermore, media-induced panic is significant inboth exceptionally bearish and bullish conditions considering S&amp;amp;P 500 dailyreturns, but is also exhibiting low impact.

The “Third Generation” Constructivism: Framing and Communication

The research is devoted to the consideration of some new methodological trends in the constructivism of the “third generation” of scientists. Its representatives, while remaining committed to the basic postulates of constructivism, at the same time turned their main attention to issues related to the theory of communication to framing as one of the ways of constructing reality and saturating it with certain meanings. Based on Martin Buber’s theory of dialogue, constructivists draw attention to the dangers of universalism in the study of politics as a prerequisite and even a manifestation of the ideological fixation of certain concepts, positions, and theses. Meanwhile, the formation of a community as “we” presupposes a pluralism of approaches and recognition of different points of view, and at the same time “cleaning” the information space of many frames that allow, through the manipulation of “fakes,” stereotypes and false stories, to form an idea of reality that significantly distorts it and even modifying.

Spatial and historical drivers of fake news diffusion: Evidence from anti-Muslim discrimination in India

What drives the propagation of discriminatory fake news? To answer this question, this paper focuses on India at the onset of the COVID-19 pandemic: on March 30, a Muslim convention (the Tablighi Jamaat) in New Delhi became publicly recognized as a COVID hotspot. Using Twitter data, we build a comprehensive novel dataset of georeferenced tweets to identify anti-Muslim fake news. First, we document that fake news about Muslims intentionally spreading the virus spiked after March 30. Then, we investigate the geographical and historical determinants of the spread of fake news in a difference-in-difference setting. We find that the diffusion of anti-Muslim false stories was more pronounced (i) in districts closer to New Delhi, suggesting that fake news spread spatially; and (ii) in districts exposed to historical attacks by Muslim groups, suggesting that the propensity to disseminate fake news has deep-rooted historical origins.

Rape Myths in the European Court of Human Rights’ Non-Refoulement Case Law on Sexual and Gender-Based Violence

Abstract The criminal justice and the refugee/human rights systems operate within different procedural and substantive frameworks. However, analysis of the European Court of Human Rights’ case law reveals a significant parallel between the two, namely judges’ acceptance of ‘rape myths’ in making their decisions. Criminal justice scholarship has defined rape myths as stereotyped and false beliefs about rape (including about victims and perpetrators). This article translates the concept to the refugee/human rights context and extends it to other forms of sexual or gender-based violence (SGBV) as well. It identifies four specific SGBV myths in the court’s non-refoulement case law: non-reporting of SGBV in the country of origin equals non-exhaustion of local remedies or protection (institutional scope: section 4.1); the existence of a private (male) support network suffices to protect an applicant from SGBV (interpersonal scope: section 4.2); resourceful applicants do not need protection against SGBV (personal scope: section 4.3); and any vagueness, incompleteness, or inconsistency in SGBV disclosures indicates a false or exaggerated story (narrative scope: section 4.4). These types of reasoning not only lack evidence-based grounds, but also demonstrate a striking lack of understanding of the nature of SGBV and the protection needs of its survivors/victims. In theory, SGBV has been recognized as a form of ill-treatment deserving protection from refoulement. In practice, however, access to this protection is hindered by a tendency to use SGBV myths in (credibility) assessments of applicants who fear ill-treatment on the basis of SGBV. While the exact meaning of gender-sensitive non-refoulement assessments remains undefined, it cannot entail the practices of SGBV myth acceptance uncovered in this article.

Fake News Identification for Web Scrapped Data

Majority of the people get affected with misleading stories spread through different posts on social media and forward them assuming that it is a fact. Nowadays, Social media is used as a weapon to create havoc in the society by spreading fake news. Such havoc can be controlled by using machine-learning algorithms. Various methods of machine learning and deep learning techniques are used to identify false stories. There is a need for identification and controlling of fake news posts that have increased in alarming rate. Here we use Passive-Aggressive Classifier for fake news identification. Two datasets, Kaggle fake news dataset and as well as dynamically web scrapped dataset from politifact.com website. We achieved 88.66% accuracy using Passive Aggressive Classifier.

Penguatan Edukasi Hukum Dampak Informasi Informasi Hoax di Media Sosial Era Masyarakat Industri 4.0

The rapid development of information and communication technology is directly proportional to the development of technology, especially the internet. These developments have more or less led to positive things, but of course there are negative impacts that come with it. The existence of the internet, coupled with the culture that has developed in the new public space, makes it difficult for people to differentiate between factual information and hoax information. The phenomenon of hoax information in Indonesia has caused divisions among the nation's children, both in the name of religion, ethnicity and class. Hoax information can literally be interpreted as a hoax, joke, deceptive plan, telling a false story, misleading publicity, or business fraud via print or electronic media. Based on this phenomenon, we feel the need to educate the public about the impact of spreading hoax information. Considering that the role of the Government is not strong enough to work alone, the community and various parties must participate in helping fight HOAX INFORMATION. The method that will be used in this community service is through outreach activities to strengthen legal education by discussing and providing exposure regarding the risks from the spread of hoax information which will be held at Pengajian Azzahra RW 7, Karang Besuki Village.&#x0D;

New Directions in the Historiography of Indonesia's Mass Killings, 1965–66

Abstract: The article traces developments in the historiography of the mass killing of Indonesian communists in 1965–66. The first, journalistic analysis often suggested that the killings were a reflection of primitive savagery in Indonesia. The first scholarly analysis, by contrast, suggested that there had been a unique, acute polarization in Indonesian society which created an atmosphere of kill-or-be-killed. Subsequent research showed that the Indonesian army played a major role in directing the killings and in fomenting antagonism with false stories of communist brutality. More recent research indicates the importance of local antagonisms, often not directly related to ideology, in leading people to use the pogroms as a chance to advance their own interests. This development is similar to the shift in the historiography of the holocaust from intentionalism to functionalism.

Infodemic in the era of the pandemic

Abstract The COVID-19 pandemic has not only brought considerable challenges to public health, but also diffusions of fake news on social media platforms, taking on the moniker of “infodemic”. To have a better understanding of the infodemic, this research takes 580 pandemic-related fake news stories from two Taiwanese fact-checking platforms and 180,000 real news articles from four major newspapers in Taiwan spanning from January 2020 to August 2021. The paper presents lexical usages and discourses of fake news from the perspective of corpus-assisted discourse study. Keyword comparison and collocational network analysis are adopted as the major analytic framework to identify keyness keywords and to uncover the discourses embedded in COVID-19 fake news. Results suggest that pandemic-related fake news tend to emphasize the themes of virus, vaccine, and immunity regarding the content keywords. Personal pronouns that differentiate us and them, conjunctions used to construct causal explanations, time-frames that denote a confirmed social fact in false stories are also prominent lexical usages in COVID-19 fake news. Notably, verbs related to promoting action and expressions of polite requests are often fabricated in fake news messages. Collocational network reveals five main themes in pandemic-related fake news: virus, vaccine and vaccination, symptom, food and drink, caution and warning and mask wearing. This paper concludes that more than simply differentiating between true and false, fake news involve miscellaneous discursive constructions of existing political, social, and cultural dimensions within alternative and unverified reality.

FAKE NEWS DETECTOR USING DEEP LEARNING

In recent years, there has been an increase in worry about the presence of false news on the internet. Real and false news have becoming harder to identify as social media and other online platforms have rapidly replaced print media as many peoples major news sources. Techniques for natural language processing (NLP) have been developed to help identify and categorize bogus news in attempt to solve this problem. Examining the language used in news items is one method of spotting false information. Large datasets of news stories may be used to train NLP models to find linguistic patterns that are suggestive of false news. For instance, false news stories may employ dramatic or exaggerated language, or they may not cite reliable sources to back up their assertions. NLP models may be trained to recognize these patterns and spot news stories that are probably fraudulent by utilizing machine learning methods. One other method for spotting false news is to examine the social environment in which news pieces are circulated. The sharing and consumption of news stories by users is extensively documented by social media companies. NLP algorithms can spot stories that are probably false by looking for trends in social media activity. A sign that a news story is phony can be, for instance, if it is being shared quickly by a lot of bot accounts. Having access to good training data is crucial for developing an NLP-based false news detector. Real and false news stories should also be included in this data, along with information on how the pieces were shared and read on social media. Researchers may create algorithms that can precisely distinguish between real and false news stories by training NLP models on this data. Overall, the development of NLP algorithms for false news identification is a crucial milestone in the fight against the spread of untruth on the internet. We can create tools that assist individuals identify between true and false news and, in turn, foster a more informed and involved society by utilizing the power of machine learning and natural language processing.

Impact of fake news on social networks during COVID-19 pandemic in Spain

PurposeThe pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.Design/methodology/approachA quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.FindingsFake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country’s situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.Research limitations/implicationsThe main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.Originality/valueThis research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

The role of individual features of memory and impulsiveness in telling a true or false story in a realistic, clear, and reconstructible way.

The aim of the present study was to explore whether there was an interaction effect between such personal aspects and veracity on realism, clarity, and reconstructability of the story. A total of 158 participants took part in the experiment and were asked to tell a truth and a lie during an interview (veracity condition). They filled in a questionnaire measuring their metamemory performance and their level of functional and dysfunctional impulsivity. A k-means cluster analysis on metamemory and impulsivity was conducted, and three clusters were obtained: controlled-memory inefficient, controlled-memory efficient, and impulsive-average memory. The results showed that participants scored higher on all three reality monitoring criteria when telling the truth than when lying. Further, a cluster membership by veracity interaction for realism was also significant, but when telling the truth, there was no difference between clusters in terms of realism used in the explanation. Follow-up analyses showed that, when lying, the level of realism in the story was significantly higher for people belonging to the cluster "impulsive-average memory" than for people belonging to the cluster "controlled-memory efficient", a result that seems to indicate that people with good memory and can control dysfunctional impulsivity have more difficulties when lying. Research has shown that realism, clarity, and reconstructability of the story, all part of reality monitoring, can be useful to assess veracity. Generally, truth tellers obtain higher scores on all three variables than liars, but there is some variability across individuals owing to their personal characteristics. Metamemory and impulsivity also play a role in deception. From the implications of the results, the limitations of the study and suggestions for future research are also provided.

Dynamic selection of clarification channels in rumor propagation containment.

Rumors refer to spontaneously formed false stories. As rumors have shown severe threats to human society, it is significant to curb rumor propagation. Rumor clarification is an effective countermeasure on controlling rumor propagation. In this process, anti-rumor messages can be published through multiple media channels, including but not limited to online social platforms, TV programs and offline face-to-face campaigns. As the efficiency and cost of releasing anti-rumor information can vary from media channel to media channel, provided that the total budget is limited and fixed, it is valuable to investigate how to periodically select a combination of media channels to publish anti-rumor information so as to maximize the efficiency (i.e., make as many individuals as possible know the anti-rumor information) with the lowest cost. We refer to this issue as the dynamic channel selection (DCS) problem and any solution as a DCS strategy. To address the DCS problem, our contributions are as follows. First, we propose a rumor propagation model to characterize the influences of DCS strategies on curbing rumors. On this basis, we establish a trade-off model to evaluate DCS strategies and reduce the DCS problem to a mathematical optimization model called the DCS model. Second, based on the genetic algorithm framework, we develop a numerical method called the DCS algorithm to solve the DCS model. Third, we perform a series of numerical experiments to verify the performance of the DCS algorithm. Results show that the DCS algorithm can efficiently yield a satisfactory DCS strategy.