Causality-based Social Media Analysis for Normal Users Credibility Assessment in a Political Crisis

Abstract

Information trustworthiness assessment on political social media discussions is crucial to maintain the order of society, especially during emergent situations. The polarity nature of political topics and the echo chamber effect by social media platforms allow for a deceptive and a dividing environment. During a political crisis, a vast amount of information is being propagated on social media, that leads up to a high level of polarization and deception by the beneficial parties. The traditional approaches to tackling misinformation on social media usually lack a comprehensive problem definition due to its complication. This paper proposes a probabilistic graphical model as a theoretical view on the problem of normal users credibility on social media during a political crisis, where polarization and deception are keys properties. Such noisy signals dramatically influence any attempts for misinformation detection. Hence, we introduce a causal Bayesian network, inspired by the potential main entities that would be part of the process dynamics. Our methodology examines the problem solution in a causal manner which considers the task of misinformation detection as a question of cause and effect rather than just a classification task. Our causality-based approach provides a practical road map for some sub-problems in real-world scenarios such as individual polarization level, misinformation detection, and sensitivity analysis of the problem. Moreover, it facilitates intervention simulations which would unveil both positive and negative effects on the deception level over the network.

Comparison and Research on the Applicability of Combining Information of Chinese and Foreign Literary Genres Based on Data Analysis

“…erefore, this section discusses the current state of research on literary production, mainly in terms of the current state of research on poetry production. [12] proposed a poetry generation method, but it only uses the random combination of phrases to generate poetry.…”

“…Reference [6] combines the attention-based mechanism of neural network machine translation method. Attention based Neural Machine Translation Network (ANMT) [12] is applied to the generation of song lyrics, which treats song lyrics generation as a translation problem, and takes the generated verses as the input and the verses to be generated as the target output for poetry generation. iPoet is a poetry generation system based on the encoder-decoder framework [13], which generates poems by Hafez and it is a program that generates any number of poems based on a user-provided theme [14], which implements the generation of English poems based on the encoder-decoder framework.…”

Fake news detection: a systematic literature review of machine learning algorithms and datasets

“…Another relevant feature that is being widely used and favors the spread of fake news is the resemblance to plausible news, confusing people and making it challenging to combat false information dissemination (Islam et al, 2020). Furthermore, it is noteworthy that the absence or even only a delay in a disclaimer by official entities involved in some rumor, which is false but convincing, favors its dissemination (Abouzeid et al, 2019). Ruchansky, Seo, and Liu (2017) claim that many stakeholders profit from the publication of fake news online because the more provocative the news is, the greater the response and the greater its yield.…”

Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization

“…Progressively, others gave attention to the broader range of the problem (Sharma et al 2019;Shu et al 2017). Moreover, rumor detection (Zhang et al 2015), malicious accounts classification (Zannettou et al 2019;Shao et al 2018), and the causal aspects of misinformation (Abouzeid et al 2019) have been discussed. However, the majority of these methods are highly depending on linguistic or local features which cause a lack of generality in the final resolution.…”