Microblog Rumor Detection Based on Comment Sentiment and CNN-LSTM

Lv, Sheng; Zhang, Haijun; He, Hong; Chen, Bingcai

doi:10.1007/978-981-15-0187-6_17

Cited by 14 publications

(14 citation statements)

References 9 publications

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“…Little research has been undertaken to study the psychology of rumors and rumor-correcting in the context of China. Existing studies of rumors in China have mostly focused on rumor detection on social media with automated algorithms (e.g., Lv et al, 2020 ; Z. Wang & Guo, 2020 ). Nevertheless, different socioeconomic, political, and media environments create disparate levels of susceptibility to rumors and misinformation ( Kwon et al, 2016 ; Kwon & Rao, 2017 ; Oh et al, 2018 ; X. Wang & Song, 2020 ).…”

mentioning

confidence: 99%

The “Parallel Pandemic” in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media

Song

Kwon

Yin

et al. 2021

American Behavioral Scientist

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Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.

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mentioning

confidence: 99%

The “Parallel Pandemic” in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media

Song

Kwon

Yin

et al. 2021

American Behavioral Scientist

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“…some of them are discussed in this section. Sheng et al [16] explained the finding of a rumor based on consumer opinion. To achieve the predictive process, they used a convolution neural network with LSTM (CNN-LSTM).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fitness: using the conditions (16), every solution's fitness value is appraised after initializing the candidate solutions and opposite solutions. Fitness function is defined using the Eq.…”

Section: Opposition Based Learning (Obl)mentioning

confidence: 99%

An efficient sentiment analysis methodology based on long short-term memory networks

Shobana

Murali

2021

Complex Intell. Syst.

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Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

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“…There are a variety of existing techniques for rumor detection on social media, which can be divided into five main categories: content-based approaches [3]- [5], user-profile based approaches [6], [7], comment-based approaches [8], [9], propagation-based approaches [10], [11], and hybrid approaches [8], [12], [13]. Most content-based approaches utilize some traditional linguistic features such as topic features [14], term frequency [15] and bag-of-word [10], [11] for rumor detection.…”

Section: Introductionmentioning

confidence: 99%

“…The comment-based approaches utilize features extracted from comments such as the term frequency-inverse document frequency (TF-IDF) features [10], stance of the comment [7] or comment sentiment [9] to reduce the feature dimension of comments for rumor detection. Comments from the same source content are usually organized as a tree structure due to the comments posted along with the diffusion of source content [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Feature Fusion for Rumor Detection on Twitter

Luo

Zheng³

2021

IEEE Access

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The increasing popularity of social media has made the creation and spread of rumors much easier. Widespread rumors on social media could cause devastating damages to society and individuals. Automatically detecting rumors in a timely manner is greatly needed but also very challenging technically. In this paper, we propose a new deep feature fusion method that employs the linguistic characteristics of the source tweet text and the underlying patterns of the propagation tree of the source tweet for Twitter rumor detection. Specifically, the pre-trained Transformer-based model is applied to extract context-sensitive linguistic features from the short source tweet text. A novel sequential encoding method is proposed to embed the propagation tree of a source tweet into the vector space. A convolutional neural network (CNN) architecture is then developed to extract temporal-structural features from the encoded propagation tree. The performance of the proposed deep feature fusion method is evaluated with two public Twitter rumor datasets. The results demonstrate that the proposed method achieves significantly better detection performance than other state-of-the-art baseline methods.

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Microblog Rumor Detection Based on Comment Sentiment and CNN-LSTM

Cited by 14 publications

References 9 publications

The “Parallel Pandemic” in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media

The “Parallel Pandemic” in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media

An efficient sentiment analysis methodology based on long short-term memory networks

Deep Feature Fusion for Rumor Detection on Twitter

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