Minimizing Misinformation Profit in Social Networks

Chen, Tiantian; Liu, Wenjing; Fang, Qizhi; Guo, Jianxiong; Du, Ding‐Zhu

doi:10.1109/tcss.2019.2944120

Cited by 19 publications

(7 citation statements)

References 25 publications

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“…More broadly, these findings help illuminate how misinformation can serve a functional role in reinforcing oppressive social hierarchies. Past work has highlighted how external State actors spread misinformation to increase local intergroup conflict, for example Russia's actions during the 2016 US election (Linvill et al, 2019;Linvill & Warren, 2020), or that the spreading of misinformation may be driven by profit motives (Chen et al, 2019). Hierarchy-enhancing misinformation is conversely more likely to be spread by internal political actors, especially members of high status groups, with the intent of inducing feelings of group-based threat which drive engagement in group-based domination and hierarchy maintenance (Duckitt & Sibley, 2010).…”

Section: Discussionmentioning

confidence: 99%

Hierarchy-Enhancing Misinformation: Social Dominance Motives Are Uniquely Associated With Republicans’ Belief In and Sharing of Election-Related Misinformation

Lees¹,

Parker²

2021

Preprint

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The aftermath of the 2020 US Presidential election saw a deluge of election-related misinformation which falsely asserted that the election was “stolen” from Donald Trump. Since then a majority of Republicans have consistently expressed belief in this misinformation, despite no evidence for its veracity and its motivating role in the January 6th, 2021 attack on the US Capitol. Here we present evidence, using a repeated-measures design (N = 355) across a highly generalizable stimulus set, that Republicans’ support for 2020 US election-related misinformation and willingness to share it on social media are uniquely associated with social dominance motives, along with conspiracy mentality and party identification strength. We find little evidence that right-wing authoritarianism is associated with the belief in or sharing of election-related misinformation, and that cognitive reflectiveness is only associated with sharing, but not belief. We introduce the theoretical lens of Hierarchy-Enhancing Misinformation to interpret these findings, arguing that election-related misinformation is best understood as a functional mechanism by which group-based dominance hierarchies are socially and psychologically reinforced.

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Section: Discussionmentioning

confidence: 99%

Hierarchy-Enhancing Misinformation: Social Dominance Motives Are Uniquely Associated With Republicans’ Belief In and Sharing of Election-Related Misinformation

Lees¹,

Parker²

2021

Preprint

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“…In [82], an activity profit is assigned to each edge. The goal is to identify a set of nodes for the truth campaign so that high profit edges become more protected and less likely to be used to spread misinformation.…”

Section: Behaviour-aware Methodsmentioning

confidence: 99%

Minimizing the spread of misinformation in online social networks: A survey

Zareie

Sakellariou

2021

Journal of Network and Computer Applications

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“…t i and P(w n |c) are the likelihood of the word "n" being presented in class a. e latter probability is derived using the training data using the Ref. (5):…”

Section: Multinomial Naïve Bayes (Mnb)mentioning

confidence: 99%

“…Recently, during COVID-19 times, numerous amount of misinformation is shared on social media platforms. Misinformation related to vaccines is posted by fear mongers, which produce a threat for the life of people across the globe [5].…”

Section: Introductionmentioning

confidence: 99%

NNPCov19: Artificial Neural Network-Based Propaganda Identification on Social Media in COVID-19 Era

Khanday

Bhushan

Jhaveri

et al. 2022

Mobile Information Systems

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The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining “Term Frequency (TF)/Inverse Document Frequency (IDF),” “Bag of Words,” and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.

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Minimizing Misinformation Profit in Social Networks

Cited by 19 publications

References 25 publications

Hierarchy-Enhancing Misinformation: Social Dominance Motives Are Uniquely Associated With Republicans’ Belief In and Sharing of Election-Related Misinformation

Hierarchy-Enhancing Misinformation: Social Dominance Motives Are Uniquely Associated With Republicans’ Belief In and Sharing of Election-Related Misinformation

Minimizing the spread of misinformation in online social networks: A survey

NNPCov19: Artificial Neural Network-Based Propaganda Identification on Social Media in COVID-19 Era

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