Enhancing prediction of user stance for social networks rumors

Abstract

The spread of social media has led to a massive change in the way information is dispersed. It provides organizations and individuals wider opportunities of collaboration. But it also causes an emergence of malicious users and attention seekers to spread rumors and fake news. Understanding user stances in rumor posts is very important to identify the veracity of the underlying content as news becomes viral in a few seconds which can lead to mass panic and confusion. In this paper, different machine learning techniques were utilized to enhance the user stance prediction through a conversation thread towards a given rumor on Twitter platform. We utilized both conversation thread features as well as features related to users who participated in this conversation, in order to predict the users’ stances, in terms of supporting, denying, querying, or commenting (SDQC), towards the source tweet. Furthermore, different datasets for the stance-prediction task were explored to handle the data imbalance problem and data augmentation for minority classes was applied to enhance the results. The proposed framework outperforms the state-of-the-art results with macro F1-score of 0.7233.

Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022

“…Previous research has primarily focused on evaluating automated labeling for sentiment analysis [ 21 – 23 ]. Although lexicon-based models are commonly used for stance detection automatic labeling [ 3 , 5 , 6 ], few studies have specifically evaluated the performance of lexicon-based models for stance analysis [ 24 ].…”

“…While both stance detection and sentiment analysis have been extensively used in analyzing social media posts, there is a need to evaluate the performance of automated labeling approaches, especially in the domain of stance detection [ 2 , 3 ]. Traditionally, sentiment analysis and stance detection models were developed using hand-labeled data, which is labor-intensive and time-consuming.…”

“…Traditionally, sentiment analysis and stance detection models were developed using hand-labeled data, which is labor-intensive and time-consuming. In recent years, there has been a shift towards using lexicon-based models for automatic labeling, such as VADER and TextBlob [ 3 ]. These models assign positive, neutral, or negative polarities to words in a sentence, enabling automated sentiment or stance classification [ 4 – 6 ].…”