Isolating Rumors Using Sentiment Analysis

Sivasangari, V.; Mohan, A. K.; Suthendran, K.; Sethumadhavan, M.

doi:10.13052/2245-1439.7113

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…We also introduced the variable AVERAGE that is the mean value of the net sentiment scores of all the texts posted in a time period. VADER sentiment analysis package has been employed widely across different disciplines, see Pano and Kashef (2020), Shelar and Huang (2018), Sivasangari et al (2018), and Oliveira et al (2016).…”

Section: Methodsmentioning

confidence: 99%

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“I just like the stock”: The role of Reddit sentiment in the GameStop share rally

Long

Xie

Yarovaya

2022

The Financial Review

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This paper investigates the role played by the social media platform Reddit in the events around the GameStop (GME) share rally in early 2021. In particular, we analyze the impact of discussions on the r/WallStreetBets subreddit on the price dynamics of the American online retailer GameStop.We customize a sentiment analysis dictionary for Reddit platform users based on the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis package and perform textual analysis on 10.8 million comments. The analysis of the relationships between Reddit sentiments and 1-, 5-, 10-, and 30-min GameStop returns contribute to the growing body of literature on "meme stocks" and the impact of discussions on investment forums on intraday stock price movements.

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Section: Methodsmentioning

confidence: 99%

“…VADER sentiment analysis package has been employed widely across different disciplines, see Pano and Kashef (2020), Shelar and Huang (2018), Sivasangari et al. (2018), and Oliveira et al. (2016).…”

Section: Methodsmentioning

confidence: 99%

“I just like the stock”: The role of Reddit sentiment in the GameStop share rally

Long

Xie

Yarovaya

2022

The Financial Review

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“…Likewise, Sivasangari et al calculated strength and sentiment category for textual data using rule-based heuristics approach. The researcher introduced VADER sentiment analysis to obtain the sentiment lexicon score for scraped dataset for distinguishing between rumour and genuine content [45]. SVM (Support Vector Machines) is another approach used for rumour detection along with sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Emotion Infused Rumour Detection Model using LSTM

Sharma,

Sethi,

Ahuja

2024

IJCDS

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Twitter now 'X', is a highly favored platform for sharing brief messages, known as tweets, read and shared among users at a rapid pace. Hence, the dissemination of information occurs quickly within the community of users in network. Twitter's unregulated environment provides a suitable platform for individuals to share and circulate unverified information; this propagation of rumours can greatly affect society. The detection of rumour accurately on Twitter from tweets is a crucial task. In this study, we suggested an Emotion Infused Rumour Detection model based on an LSTM model that employs tweet text and twenty-one distinct linguistic, user, post, and network features to classify between rumour and non-rumour tweets. comparison of the proposed model i.e. Emotion Infused Detection model using LSTM was done with two different deep learning models to check the achieved outcomes. The findings of the evaluations exhibit the supremacy of the deep learning-based model for identifying rumours. The suggested Emotion Infused Rumour Detection model, which uses an LSTM model, earned an F1-score of 0.91 in identifying rumour and non-rumour tweets, outperforming the state-of-the-art findings. The suggested approach can lessen the influence of rumours on society, prevent loss of life and money, and increase users' confidence in social media platforms. The model proposed has the potential to promptly and accurately recognize tweets containing rumours, aiding in the prevention of the spread of misinformation.

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“…e results of sentiment analysis reflect the real position of users, which can be used to evaluate the credibility of microblog content from the perspective of many users. Sivasangari et al divided the score value of the emotional dictionary for the selected data to separate false content more accurately [32]. Wang and Guo encoded sentiment information into the time-series division process, and they completed the content credibility evaluation by capturing the changes of context and sentiment information over time [33].…”

Section: Content Credibility Evaluationmentioning

confidence: 99%

A Microblog Content Credibility Evaluation Model Based on the Influence of Sentiment Polarity

Yao

Xing

2022

Mobile Information Systems

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False content in microblogs affects users’ judgment of facts. An evaluation of microblog content credibility can find false information as soon as possible, which ensures that social networks maintain a positive environment. The influence of sentiment polarity can be used to analyze the correlation between sentiment polarity in comments and Weibo content through semantic features and sentiment features in comments, to improve the effect of content credibility assessment. This paper proposes a Weibo content credibility evaluation model, CEISP (Credibility Evaluation based on the Influence of Sentiment Polarity). The semantic features of microblog content are extracted by a bidirectional-local information processing network. Bidirectional long short-term memory (BiLSTM) is used to mine the sentiment features of comments. The attention mechanism is used to capture the impact of different sentiment polarities in comments on microblog content, and the influence of sentiment polarities is obtained for the credibility assessment of microblog content. The experimental results on real datasets show that the evaluation performance of the CEISP model is improved compared with the comparison model. Compared with the existing Att-BiLSTM model, the evaluation accuracy of the CEISP model is improved by 0.0167.

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Isolating Rumors Using Sentiment Analysis

Cited by 14 publications

References 16 publications

“I just like the stock”: The role of Reddit sentiment in the GameStop share rally

“I just like the stock”: The role of Reddit sentiment in the GameStop share rally

Emotion Infused Rumour Detection Model using LSTM

A Microblog Content Credibility Evaluation Model Based on the Influence of Sentiment Polarity

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