Fake Posts Research Articles

Faking the war: fake posts on Turkish social media during the Russia–Ukraine war

This study aims to analyze the fake posts circulated on Turkish social media during the Russia–Ukraine war. With advancing technology, social media platforms have a profound impact on the way we perceive and interpret events and make us question the accuracy of information generated about international events such as wars. While the Russia–Ukraine war constitutes an important turning point in international relations, the reflection of these events on social media is also seen in fake posts. In this context, the main purpose of this study is to identify the common themes of fake social media posts and to reveal the general context of these posts on social media. In addition, the study aims to analyze the fake content circulating on Turkish social media and to reveal the emerging polarized discourses through the identified themes. The research revolves around five main themes that feed polarization: war reporting, ideological misrepresentation, humor, hate speech, and conspiracy theories. The findings show that fake content is particularly concentrated around ideological polarization and antagonisms. It was also found that misinformation and decontextualized humor blurred the true context of the war and that fake content combined with hate speech and conspiracy theories distorted the context of the war.

Fake Social Media Post Detection by Using Deep Learning Algorithm

In today's digital age, social media serves as both a boon and a bane, offering immense opportunities for personal growth while also being a breeding ground for misinformation and deception. The rise of sophisticated multimedia manipulation techniques has blurred the line between reality and fiction, presenting significant challenges to discerning genuine content from fabricated ones. This paper presents an overview of the escalating threat posed by fake posts on social media and proposes a novel approach for their detection. The proposed method leverages advanced technologies such as Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs) to distinguish between authentic and manipulated posts. Unlike traditional approaches reliant on manual feature extraction, our framework employs deep learning methodologies for more nuanced pattern recognition and generalization to unseen data. By combining ELA for initial image analysis and CNNs for deep feature extraction, followed by classification using Support Vector Machines (SVMs) and K-Nearest Neighbors (KNNs), we achieve robust detection of fake images .Experimental results demonstrate the effectiveness of our approach, with a peak accuracy of 91.3% using Residual Networks and KNN. This method offers a promising solution to mitigate the proliferation of fake images on social media platforms, thereby combating the spread of misinformation, propaganda, and chaos.

An Efficient Fake News Detection using Machine Learning

Fake news and hoaxes have been there since before the advent of the Internet. The widely accepted definition of Internet fake news is: fictitious articles deliberately fabricated to deceive readers”. Social media and news outlets publish fake news to increase readership or as part of psychological warfare. In general, the goal is profiting through clickbaits. Clickbaits lure users and entice curiosity with flashy headlines or designs to click links to increase advertisements revenues. This exposition analyzes the prevalence of fake news in light of the advances in communication made possible by the emergence of social networking sites. The purpose of the work is to come up with a solution that can be utilized by users to detect and filter out sites containing false and misleading information. In this paper we have used simple and carefully selected features of the title and post to accurately identify fake posts. The experimental result shows maximum of 87.04% accuracy for logistic model.

Robust fake-post detection against real-coloring adversaries

The viral propagation of fake posts on online social networks (OSNs) has become an alarming concern. The paper aims to design control mechanisms for fake post detection while negligibly affecting the propagation of real posts. Towards this, a warning mechanism based on crowd-signals was recently proposed, where all users actively declare the post as real or fake. In this paper, we consider a more realistic framework where users exhibit different adversarial or non-cooperative behaviour: (i) they can independently decide whether to provide their response, (ii) they can choose not to consider the warning signal while providing the response, and (iii) they can be real-coloring adversaries who deliberately declare any post as real. To analyse the post-propagation process in this complex system, we propose and study a new branching process, namely total-current population-dependent branching process with multiple death types. At first, we compare and show that the existing warning mechanism significantly under-performs in the presence of adversaries. Then, we design new mechanisms which remarkably perform better than the existing mechanism by cleverly eliminating the influence of the responses of the adversaries. Finally, we propose another enhanced mechanism which assumes minimal knowledge about the user-specific parameters. The theoretical results are validated using Monte-Carlo simulations.

When falsehood wins? Varied effects of sensational elements on users’ engagement with real and fake posts

Based on a quantitative content analysis of microblogs on COVID-19 that is linked to the actual Weibo user engagement (comments, reposts, and likes) they received, this study investigates the role of generic/formal frames, emotional appeals, and visual elements in people's varied levels of engagement with fake and real posts. Results revealed that relative to real posts, fake posts tended to focus more on COVID incidents that happened outside of China, utilize more episodic and human-interest frames, rely more on anger and disgust emotions, and feature more pictures. More importantly, although fake posts initially received fewer user responses than real posts, they earned significantly more reactions through employing sensational elements such as anger, conflict, and morality in their content. Most of these post-level characteristics, however, exerted minimal impact on real microblogs. Consequently, as the emotionally charged and sensationally framed fake posts drive more users to comment on and repost them, fake news may diffuse faster than real news and reach a larger audience.

УКРАЇНСЬКА ІСТОРІОГРАФІЯ НА ШЛЯХУ ДІДЖИТАЛІЗАЦІЇ

Relevance and purpose of the article. In response to information and globalization processes in the modern world, to the occupation-genocide war of Russia against Ukraine reconstruction and transformation of sciences is ongoing, including Ukrainian historiography in electronic form, which is accompanied by an update of methodological, cognitive-scientific, educational, ideological and educational functions, enrichment of the source base at the expense of electronic information resources, expansion of the issues of scientific research studies, transfer of research results into digital form, and hence the new format of their presentations and introduction into social practices. The research methodology is based on taking into account the assets of predecessors, systematic adherence to the principles of historicism, continuity, anthropological, sociocultural and critical-analytical approaches, the involvement of methods of historiographical analysis and synthesis, comparative studies, methods of historical-chronological, historical-genetic knowledge and personal reflections. Presentation of the material built in four blocks: a) methodological and terminological foundations of the formation of electronic historiography; b) formation of historiographical sources in digital format and involvement of their information resources; c) creation of electronic historiographical texts; d) traditional and public historiography in the digital information space. Systematized information about the format and content of electronic web portals and websites of academic historical institutions, historical faculties and departments of national universities, creation of electronic texts based on the involvement of sources and literature on electronic carrier, formation of public history in digital format. Highlighted the value of electronic information resources of libraries, archives, museums for the development of historiography, the historiographical context of the concepts «electronic source» and «informational electronic resource» is clarified. Separated place and role in the structure of the source base historiography of electronic information resources of social networks, Internet communities, bloggers, in particular Facebook, YouTube, WhatsApp, Viber, Instagram, Twitter, Telegram, Messenger etc. Expressed a warning about the need to be critical of information, drawn from social networks, checking their authenticity, comparison with other sources, specified, th at they are often used for disinformation and manipulation of consciousness through fake posts, for compromising historians, which was especially evident in the conditions of the current war and Russia's informational aggression against Ukraine, the «war of histories», the imposition of the paradigm of «one nation» and «Russian peace». Conclusions and suggestions. Formation of electronic Ukrainian historiography, involvement in scientific studies of sources on digital media, the creation of electronic texts is intended to contribute to the new quality of research work, formation of a new generation of scientists as a response to the challenges of the digital revolution, globalization of the modern world, the information explosion and Russia's war against Ukraine. It is important to continue further research into the problems of the formation and development of digital historiography, updating the methodology, increasingly wider involvement of electronic information resources and increasing on this basis its role as a system of scientific knowledge and educational discipline on the development of historical science.

Security of the Media Audience in the Modern Information Space

The object of the study is the current state of the information space, the subject of the study is human security in the conditions of modern media space. The author examines in detail such aspects of the topic as the impact of media products on society, manipulative and viral technologies in the media sphere, the theory of setting the agenda, the problem of fake content in mass communications. Particular attention is paid to finding ways to identify fakes in the information field of the Internet and, in particular, social networks to ensure the media security of the user. The authors consider the dynamics of the development of the phenomenon of "post-truth" against the background of pandemic reality, analyze the differences between true news and fake, and provide a set of measures to expose false data in the media environment. The main conclusion of the study is the judgment that the mass audience invariably faces the need to filter content and recognize fakes, especially in the online environment of social networks, which requires certain procedures for monitoring and analyzing information. A special contribution of the author to the research of the topic is the conduct of a sociological study and processing of the results, which allowed us to form a model for evaluating news content in social networks to identify fake posts. The scientific novelty of the study consists in considering the previously unexplored features of the media security of the modern media audience in the information space on the example of social media resources.

An explainable model for content moderation

The spread of fake news on social media has eroded trust in traditional news outlets and institutions. In response, social media platforms have incorporated machine learning, algorithms that can learn patterns in data without explicit programming, into their content moderation systems to help remove fake posts. However, these algorithms often misinterpret language and make poor moderation decisions. We need to better understand how machine learning algorithms interpret language. Explainability is a challenge with neural networks, a popular machine-learning model inspired by how neurons communicate in the brain. In this study, we sought to develop an explainable model for content moderation comparable in accuracy to a traditional neural network, focusing on classifying real and fake news articles. We hypothesized that by identifying keywords and quantifying their contribution to an article’s credibility, we could predict credibility by summing the contributions of all the keywords in an article. We trained a convolutional neural network to classify articles using a 3,000-keyword vocabulary, achieving 85.8% accuracy. We used the Shapley additive explanations algorithm to calculate each keyword's median contribution to the model’s predictions. We created a linear model that summed the median contributions of keywords in an article, achieving a comparable 81.0% accuracy. We then examined keywords with the largest median contributions. Clickbait and COVID-19 terms correlated with fake news. Legal and political terminology correlated with real news. Our results demonstrate the potential for explainable models to improve our understanding of content moderation algorithms and fake news linguistics.

Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation

Hyper-partisan misinformation has become a major public concern. In order to examine what type of misinformation label can mitigate hyper-partisan misinformation sharing on social media, we conducted a 4 (label type: algorithm, community, third-party fact-checker, and no label) X 2 (post ideology: liberal vs. conservative) between-subjects online experiment (N = 1,677) in the context of COVID-19 health information. The results suggest that for liberal users, all labels reduced the perceived accuracy and believability of fake posts regardless of the posts' ideology. In contrast, for conservative users, the efficacy of the labels depended on whether the posts were ideologically consistent: algorithmic labels were more effective in reducing the perceived accuracy and believability of fake conservative posts compared to community labels, whereas all labels were effective in reducing their belief in liberal posts. Our results shed light on the differing effects of various misinformation labels dependent on people's political ideology.

Fake spreader is narcissist; Real spreader is Machiavellian prediction of fake news diffusion using psycho-sociological facets

Fake news reference to misleading or false articles that is been spread on social network. The diffusion of misleading information on social network causes an impact on civil life of the people in terms of psychological, financial etc., which makes them difficult to lead their life in the society. Therefore, developing a system to predict the diffusion of fake news is essential. This paper proposes a fake news diffusion prediction model involving several psycho-sociological facets of social media users. The underlying presumptions are two-fold: (a) it is essential to understand the probable impact of a posted fake news message, i.e., how many persons will be influenced by the information spread; and (b) although information propagation is a well-explored topic, most of the work has been restricted to network topological analysis. To this end, we present psycho-sociological analysis as a better alternative to understand fake propagation in a social network, with the aim to answer a few fundamental questions: (i) Who initiates fake posts on social media? (ii) Who consumes (replies to, shares, or likes) such comments? (iii) Can we model fake news diffusion better if we know the psycho-sociological traits of individuals towards fakeful content?To present psycho-sociological analysis, we bring in three different dimensions of human behavior: personality (Big-5: Openness, Agreeableness, Conscientiousness, Extraversion, and nNeuroticism), values & ethics (Schwartz model: Self-Direction, Stimulation, Hedonism, Achievement, Power, Security, Conformity, Tradition, Benevolence and Universalism), and the dark side of the personality (dark triad: Narcissism, Machiavellianism, and Psychopathy). The Big5 model describes five essential person-level traits, the values & ethics model depicts societal behavior, and the dark triad model encapsulates the dark side of human personality.We develop a suite of classifiers to detect behavioral traits. We also propose a model to predict the diffusion of fake news and classifier to classify user as fake or real. Various empirical studies are reported in this paper in order to comprehend how human behavioral traits correlate with online fake spreader and fake posting user behavior. From our empirical analysis some of the key observations found are: (i) fake posts on social media are commonly started by people with neurotic personality. (ii) People who spread political fake posts are traditional and power oriented. (iii) Fake user is extrovert and neurotic in nature, and those who post fake and real news on social networks tend to have balanced dark side behavior. The proposed models also outperform state-of-the-art with a significant margin.

Electronic Information Archival Resources as a Source Base of Ukrainian Studies

The article substantiates the source value of electronic information archival resources for the development of the Ukrainian studies; reveals Ukrainian studies context of concepts “electronic source” and “information electronic resource”; tracks transformation of traditional Ukrainian studies as an integrated knowledge system and academic discipline into electronic format. It provides systematized information about information and reference apparatus, web portals, websites of the State Archival Service, central, industry and local archives, their electronic guidebooks, pointers, card-indexes; shows their role in methods of search and detection of documents of Ukrainian studies character.The article considers problems and characterizes meaningful contents of electronic information resources of central historical archives in Kyiv and Lviv, their importance for the study of ancient history of Ukraine, its princely and cossack hetman times, era of national and cultural revival, revolutions and wars. It highlights the central state electronic archives, archives of higher authorities and management, public associations, film and photo archives named after H. Pshenychnyi, archive-museum of literature and art, archives of Kyiv and regions from an angle of possible involvement of their documentary resources into the study of the key concenters of Ukrainian studies: Ukraine-ethnos, Ukraine-nature (ecology), Ukraine-language, Ukraine-nation, Ukraine-state, Ukraine-figures, Ukraine-culture, Ukraine-spirituality, Ukraine-mentality. It is emphasized that to figure out the concenter Ukraine-world, ultimately important are digitized personal funds of Ukrainian emigration activists, Ukrainian State Center and the UPR government in exile, collection of political documents, public and cultural associations in the diaspora stored in the Central State Archives of Foreign Ukrainian Studies. In view of the fact that the problems of historical memory, dramatic pages of the Ukrainian history related to mass repressions, famines, “shot revival”, terror of the communist regime, the Ukrainian resistance movement, the Sixties and dissidents, nationalist underground, activities of the OUN and the UPA take a significant place in the Ukrainian studies domain, documentary evidence, which was saved in paper, audiovisual and electronic formats in the sectoral state archives of the Security Service of Ukraine, Ministry of Internal Affairs, the Ukrainian Institute of National Remembrance, etc., gain more and more attention. The research distinguishes place and role in the structure of the Ukrainian studies source base of electronic information resources of social networks, Internet communities, including Facebook, YouTube, WhatsApp, Viber, Instagram, Twitter, Telegram, Messenger etc., which are global, dynamic and general in nature. It expresses a warning about the need for critical attitude towards information, gained from social networks, because they are often used for disinformation through fake posts, manipulation of consciousness, for compromising public figures, which was especially manifested in the context of Russia’s occupation-hybrid warfare against Ukraine, powerful information aggression, "war of stories", imposition of the “Russian World” paradigm.The conclusion substantiates that the formation of electronic Ukrainian studies, further involvement in the source database of electronic information resources facilitate the improvement of their role as a system of scientific knowledge and academic discipline.

Fake news and declining media trust during COVID 19 pandemic

Fake news is not a new phenomenon and has been an ever-growing threat to many democracies across the globe, especially in India. This paper is an attempt to understand the growing mistrust on media among Indians during covid 19 pandemic. The health crises that emerged during the pandemic jolted many institutions including the fourth pillar of democracy, media. The study has attempted a comprehensive systematic literature review of the growing mistrust around media and tried to assess the various factors that were involved leading to mistrust. The study done during the year 2020 employed a survey and an online focus group discussion to assess the output. The results revealed that misinformation was at its peak during Covid lockdown and a large number of people shared fake posts on social media platforms, thinking it to be true.

Examining Source Effects on Perceptions of Fake News in Rural India

This paper presents a between-subjects design experiment with 478 people in India to investigate how rural and urban social media users perceive credible and fake posts, and how different types of sources impact their perceptions of information credibility and sharing behaviors. Our findings reveal that: (1) rural social media users were less adept in differentiating between credible and fake posts than their urban counterparts, and (2) source effects on trust and sharing intent manifested differently for urban and rural users. For example, fake posts from family members garnered greater trust among urban users but were trusted the least by rural users. In case of sharing Facebook posts, urban users were more willing to share fake posts from family, whereas, rural users were more inclined to share fake posts from journalists. Drawing on these findings, we propose design interventions to counteract fake news in low-resource environments of the Global South.

Platform Policies and Sellers’ Competition in Agency Selling in the Presence of Online Quality Misrepresentation

ABSTRACT On e-commerce platforms, consumers rely heavily on online reviews, sales volume, and social media discussions to infer product quality. As a result, the past decade has witnessed an explosive growth of seller-initiated misrepresentation of quality through fake reviews, fake sales, and fake posts. We develop an analytical model to investigate sellers’ competition in quality misrepresentation in agency pricing and the platform’s policies. The platform can discourage sellers’ quality misrepresentations by increasing the cost of misrepresentation or implementing a more lenient product return policy. We find that while a stricter anti-misrepresentation policy deters the misrepresentation of the high-quality seller, such a strategy may unintendedly incentivize the low-quality seller to misrepresent the quality more. Furthermore, increasing return leniency deters low-quality seller’s misrepresentation in a wider range of market conditions than increasing the misrepresentation cost. We show sellers’ online quality misrepresentation behaviors in a competitive setting, and our results have practical implications for platform policies.

Fake News Detection via Multi-Modal Topic Memory Network

With the development of the Mobile Internet, more and more people create and release multi-modal posts on social media platforms. Fake news detection has become an increasingly challenging task. Although many current works focus on constructing models extracting abstract features from the content of each post, they neglect the intrinsic semantic architecture such as latent topics, etc. These models only learn patterns in content coupled with certain specific latent topics on the training set to distinguish real and fake posts, which will suffer generalization and discriminating ability decline, especially when posts are associated with rare or new topics. Moreover, most existing works using deep schemes to extract and integrate textual and visual representation in post have not effectively modeled and sufficiently utilized the complementary and noisy multi-modal information containing semantic concepts and entities to complement and enhance each modal. In this paper, to deal with the above problems, we propose a novel end-to-end Multi-modal Topic Memory Network (MTMN), which obtains and combines post representations shared across latent topics together with global features of latent topics while modeling intra-modality and inter-modality information in a unified framework. (1) To tackle real scenarios where newly arriving posts with different topic distribution from the training data, our method incorporates a topic memory module to explicitly characterize final representation as post feature shared across topics and global features of latent topics. These two kinds of features are jointly learned and then combined to generate robust representation. (2) To effectively integrate multi-modality information in posts, we propose a novel blended attention module for multi-modal fusion, which can simultaneously exploit the intra-modality relation within each modal and the inter-modality relation between text words and image regions to complement and enhance each other for high-quality representation. Extensive experiments on two public real-world datasets demonstrate the superior performance of MTMN compared with other state-of-the-art algorithms.

Intentional or inadvertent fake news sharing? Fact-checking warnings and users’ interaction with social media content

The main social media platforms have been implementing strategies to minimize fake news dissemination. These include identifying, labeling, and penalizing –via news feed ranking algorithms– fake publications. Part of the rationale behind this approach is that the negative effects of fake content arise only when social media users are deceived. Once debunked, fake posts and news stories should therefore become harmless. Unfortunately, the literature shows that the effects of misinformation are more complex and tend to persist and even backfire after correction. Furthermore, we still do not know much about how social media users evaluate content that has been fact-checked and flagged as false. More worryingly, previous findings suggest that some people may intentionally share made up news on social media, although their motivations are not fully explained. To better understand users’ interaction with social media content identified or recognized as false, we analyze qualitative and quantitative data from five focus groups and a sub-national online survey (N = 350). Findings suggest that the label of ‘false news’ plays a role –although not necessarily central– in social media users’ evaluation of the content and their decision (not) to share it. Some participants showed distrust in fact-checkers and lack of knowledge about the fact-checking process. We also found that fake news sharing is a two-dimensional phenomenon that includes intentional and unintentional behaviors. We discuss some of the reasons why some of social media users may choose to distribute fake news content intentionally.

Manifestations of Fake News - Possible Legal and Policy Issues to Be Considered before Formulating Any Law in India

During this lockdown situation, we have witnessed array of rumors or fake news; from Amul Company shutting down its milk chilling centers to the effective use of ginger, lemon and honey to counter the virus or to dispersing or spraying of the medicine by helicopters. Clearly, the proliferation of inaccurate or misleading news is spiraling upwards, especially during COVID-19 Pandemic situation. Our mobile phones and social media accounts are flooded with fake posts, doctored videos and congenial but unverified theories (especially qua the origin of Corona Virus and its cure), which are quickly shared or forwarded, especially through Whatsapp, Tiktok and Facebook, and out of which some may tickle your fancies at one hand, but some may create tension and unrest amongst people at large. For instance, just couple of days ago, a video on social media went viral where the soldiers of two different armies were shown to be engaged in a provocative incursions and it was being claimed that Chinese soldiers are provoking the Indian army soldiers at the Ladakh Indo-China Border, but the original video was traced back to the year 2014 and pertaining to Arunachal Pradesh Border, though the Indian army has never avowed for the video as well. Such unverified claims or rumors are dangerous and have the ability to instill fear and terror in the minds of people and may cause chaos and disruption in the society and tensions between the countries. Hence, the question is that what is this concept of ‘Fake News’ and why it has assumed immense significance and it is also important to know that in what forms, it exists or reaches to us.

Influence of youtube and fake posts on youth

You Tube is an American video sharing platform which allows users to view, upload, rate, like, comment, share, add to favourites, report, and also subscribe to other users. It is a large platform for variety of user generated content in the form of video clips, music videos, TV shows, audio recordings, trailers, short films, documentaries, live streaming etc. This service is free to use and creates a great space for the users especially the youth to discover new things and stay updated. However, users do not have to sign in to access the website or the mobile app, which is leading to risks regarding circulation of inappropriate and fake content, cyber bullying, hacking or invading privacy, etc. In all these odds, You Tube still plays a crucial role in every Nettie's life which can be for entertainment, educational purpose, to get trained, to be updated, to showcase the talents, as stress busters and many more. Teenagers or youth find these videos on You Tube more relatable, attractive and influential in their day today lives. The main aim of this paper is to study and analyse the influence of YouTube posts and fake posts on the youth of Hyderabad city in Andhra Pradesh. The study is based on a survey done with the youth between the age group of 13–25 years in Hyderabad city in South of India. Results show that the respondents are highly influenced by the You Tube posts and fake identities.

Identification of Fake Posts in Instagram using Grade based Approach in Online Social Networks

Identification of Fake Posts in Instagram using Grade based Approach in Online Social Networks

Detecting Fake News in Social Media Networks

Fake news and hoaxes have been there since before the advent of the Internet. The widely accepted definition of Internet fake news is: fictitious articles deliberately fabricated to deceive readers”. Social media and news outlets publish fake news to increase readership or as part of psychological warfare. Ingeneral, the goal is profiting through clickbaits. Clickbaits lure users and entice curiosity with flashy headlines or designs to click links to increase advertisements revenues. This exposition analyzes the prevalence of fake news in light of the advances in communication made possible by the emergence of social networking sites. The purpose of the work is to come up with a solution that can be utilized by users to detect and filter out sites containing false and misleading information. We use simple and carefully selected features of the title and post to accurately identify fake posts. The experimental results show a 99.4% accuracy using logistic classifier.