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False Information (FI) is a critical societal issue, made even more pressing by our inability to mitigate its influence through correction. Researchers Johnson and Seifert (1994, Experiment 1A) penned a seminal paper on this "Continued Influence Effect" (CIE), which they observed in English-speaking participants. In their experiments, one group read a text containing FI that was later retracted (FI group), while another read the same text without FI (control group). Interestingly, even after receiving corrections, participants who read the FI were more likely than their peers to form FI-related inferences about the text. To the best of our knowledge, this finding has never been successfully directly replicated. Given the current replicability crisis plaguing the human sciences, the influence of culture on CIE and the importance of Johnson and Seifert's paradigm in this literature, the reassessment of their findings within a non-English-speaking population appears crucial. The present research investigated the direct replicability of their study with a French-speaking sample, comparing the inferences drawn by an FI group (n = 21) to those made by a control group (n = 23). The results confirm those of the original study, supporting the validity of Johnson and Seifert's paradigm (1994) and extending its applicability to a French-speaking population. • False information persists despite correction. • This effect has most often been observed in English-speaking participants. • Johnson and Seifert's study (1994) holds a central place in this literature. • We show a replication of their findings among French-speaking individuals. [ABSTRACT FROM AUTHOR]

Social networks have involved more and more users who search for and share information extensively and frequently. Tremendous evidence in Facebook, Twitter, Flickr and Google+ alike shows that such social networks are the major information sources as well as the most effective platforms for information transmission and exchange. The dynamic propagation of various information may gradually disseminate, drastically increase, strongly compete with each other, or slowly decrease. These observations had led to the present study of the spreading process of true and fake information over social networks, particularly the Facebook. Specifically, in this paper the topological structure of two huge-scale Facebook network datasets are investigated regarding their statistical properties. Based on that, an information model for simulating the true and fake information spreading over the Facebook is established. Through controlling the spreading parameters in extensive large-scale simulations, it is found that the final density of stiflers increases with the growth of the spreading rate, while it would decline with the increase of the removal rate. Moreover, it is found that the spreading process of the true–fake information is closely related to the node degrees on the network. Hub-individuals with high degrees have large probabilities to learn hidden information and then spread it. Interestingly, it is found that the spreading rate of the true information but not of the fake information has a great effect on the information spreading process, reflecting the human nature in believing and spreading truths in social activities. The new findings validate the proposed model to be capable of characterizing the dynamic evolution of true and fake information over the Facebook, useful and informative for future social science studies. [ABSTRACT FROM AUTHOR]

People are exposed to inaccurate claims and ideas every day from sources intended to inform, entertain, or do both. A large body of research has demonstrated that exposures to inaccurate statements, even when conveying obviously false ideas, can affect people's subsequent judgments. Contemporary accounts suggest that these effects may be due to people's failure to evaluate information during exposure, increasing the likelihood that false information will be encoded and available for retrieval on subsequent tasks. In three experiments, we investigated whether evaluative mindsets reduce the likelihood people are influenced by and use inaccurate statements, as well as encourage reliance on accurate understandings. In Experiment 1, participants who were instructed to engage in deliberate evaluation of potentially inaccurate statements reproduced fewer inaccurate ideas and produced more correct answers to post-reading questions than did participants who simply rated their interest in the statements. In Experiments 2 and 3, the same benefits were obtained even when participants were not consistently prompted to evaluate the statements. These results offer insight into when and how evaluation can encourage participants to rely on correct prior knowledge over presented inaccuracies, as well as what is required to establish and maintain such an evaluative mindset. [ABSTRACT FROM AUTHOR]

During emergencies, exposure to false information can increase individual vulnerability. More research is needed on how emergency management institutions understand the effects of false information and what are the various approaches to handling it. Our document analysis and 95 expert interviews in eight European countries – Germany, Italy, Belgium, Sweden, Hungary, Norway, Finland, and Estonia – show that approaches vary considerably: some have instituted central management of identifying and tackling false information while others prioritise the spreading of accurate information. A review of national practices and an analysis of recent crisis cases show that both approaches may be necessary. The diffusion of false information is strongly affected by the lack of timely and verifiable information from governments. We also find that in several countries, the emergence of false information is often associated with malicious foreign influence activities. Our study contributes to a better understanding of how the effects of false information are mitigated by the emergency management systems in Europe. [ABSTRACT FROM AUTHOR]

• With the fast-growing IoT, regular connectivity through a range of heterogeneous intelligent devices across the Social Online Networks (SON) is feasible and effective to analyze sociological principles. Therefore, increased user contributions, including web posts, videos and reviews slowly impact the lives of people in the recent past, which triggers volatile knowledge dissemination and undermine protection through gossip dissemination, disinformation, and offensive online debate. • In this paper, the proportion of trustworthy Facebook fans who post regularly in early and future popularity has been analyzed linearly using PSTIR and SFIBS methods. Facebook statistics remind us that mainstream fatigue is an important prediction principle and The mainstream fatigue principle, Besides, it shows the effectiveness of the PSTIR and SFIBS based on experimental study. With the fast-growing IoT, regular connectivity through a range of heterogeneous intelligent devices across the Social Online Networks (SON) is feasible and effective to analyze sociological principles. Therefore, Increased user contributions, including web posts, videos and reviews slowly impact the lives of people in the recent past, which triggers volatile knowledge dissemination and undermine protection through gossip dissemination, disinformation, and offensive online debate. Based on the early diffusion status, the goal of this research is to forecast the popularity of online content reliably in the future. Though conventional prediction models are focused primarily on the discovery or integration of a network functionality into a changing time mechanism has been considered as unresolved issues and it has been resolved using Predicting The Security Threats of Internet Rumors (PSTIR) and Spread of False Information Based On Sociological (SFIBS) model with sociology concept. In this paper, the proportion of trustworthy Facebook fans who post regularly in early and future popularity has been analyzed linearly using PSTIR and SFIBS methods. Facebook statistics remind us that mainstream fatigue is an important prediction principle and The mainstream fatigue principle, Besides, it shows the effectiveness of the PSTIR and SFIBS based on experimental study. [ABSTRACT FROM AUTHOR]

How do people process and evaluate falsehood of sentences? Do people need to compare presented information with the correct answer to determine that a sentence is false, or do they rely on a mismatch between presented sentence components? To illustrate, when confronted with the false sentence 'trains run on highways', does one need to know that trains do not run on highways or does one need to know that trains run on tracks to reject the sentence as false? To investigate these questions, participants were asked to validate sentences that were preceded by images (Experiments 1-3) conveying a truth-congruent or a falsehood-congruent component of the sentence (e.g., an image of tracks/highway preceding the sentence 'trains run on tracks/highways') or by words (Experiment 4) that were either sentence-congruent, truth-congruent, or both (e.g., the word 'train/tracks' preceding the sentence 'trains run on tracks/highways'). Results from four experiments showed that activating sentence-congruent concepts facilitates validation for both false and true sentences but that activating truth-congruent concepts did not aid the validation of false sentences. The present findings suggest that a detection of falsehood relies on a mismatch detection between sentence's components, rather than on the activation of true content in the context of a particular sentence. [ABSTRACT FROM AUTHOR]