A semi-supervised model for Persian rumor verification based on content information

Jahanbakhsh-Nagadeh, Zoleikha; Feizi‐Derakhshi, Mohammad‐Reza; Sharifi, Arash

doi:10.1007/s11042-020-10077-3

Cited by 18 publications

(8 citation statements)

References 44 publications

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“…Since rumors in social media are designed in many fields and require a large number of domain experts to classify rumors, the data set labeling of rumors detection is a very difficult and requires a lot of money. In theory, unsupervised learning methods And semi-supervised learning methods can solve this kind of problems very well ([ 30 ],[ 109 ],[ 41 ]). However, although there have been some studies, there are still no outstanding applications.…”

Section: Rumor Detection Methodologymentioning

confidence: 99%

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Research status of deep learning methods for rumor detection

Tan

Wang

Jia

et al. 2022

Multimed Tools Appl

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To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.

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Section: Rumor Detection Methodologymentioning

confidence: 99%

“…Much money. In addition, even for professionals in the field, labeling news as true or false is a very challenging task for them ([ 41 ]). Some works ([ 102 ]) suggest using crowdsourcing to label the rumor detection data set, but the accuracy of the labeling cannot be guaranteed.…”

Section: Potential Issues and Future Workmentioning

confidence: 99%

Research status of deep learning methods for rumor detection

Tan

Wang

Jia

et al. 2022

Multimed Tools Appl

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“…In addition, in [27], [28] a supervised learning model for rumor verification using contextual features based on contextual word embeddings, speech acts, and the writing style was presented. In [29] the authors collected unstructured data from social networks, using an application implementing different supervised and unsupervised learning classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Sierra-Linan³

et al. 2022

IJACSA

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Today, web content such as images, text, speeches, and videos are user-generated, and social networks have become increasingly popular as a means for people to share their ideas and opinions. One of the most popular social media for expressing their feelings towards events that occur is Twitter. The main objective of this study is to classify and analyze the content of the affiliates of the Pension and Funds Administration (AFP) published on Twitter. This study incorporates machine learning techniques for data mining, cleaning, tokenization, exploratory analysis, classification, and sentiment analysis. To apply the study and examine the data, Twitter was used with the hashtag #afp, followed by descriptive and exploratory analysis, including metrics of the tweets. Finally, a content analysis was carried out, including word frequency calculation, lemmatization, and classification of words by sentiment, emotions, and word cloud. The study uses tweets published in the month of May 2022. Sentiment distribution was also performed in three polarity classes: positive, neutral, and negative, representing 22%, 4%, and 74% respectively. Supported by the unsupervised learning method and the K-Means algorithm, we were able to determine the number of clusters using the elbow method. Finally, the sentiment analysis and the clusters formed indicate that there is a very pronounced dispersion, the distances are not very similar, even though the data standardization work was carried out.

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“…Some of the important studies in this area are mentioned here. Many deep learning models such as LSTM, GRU, and RNN have been used to detect rumors on Twitter [13,[25][26][27]. Ma et al [28] applied Recurrent Neural Network Model using deep learning in their early work to identify and verify rumors.…”

Section: Deep Learning Based Approachesmentioning

confidence: 99%

Rumor conversations detection in twitter through extraction of structural features

et al. 2021

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Twitter is one of the most popular and renowned online social networks spreading information which although dependable could lead to spreading improbable and misleading rumors causing irreversible damage to individuals and society. In the present paper, a novel approach for detecting rumor-based conversations of various world events such as real-world emergencies and breaking news on Twitter is investigated. In this study, three aspects of information dissemination including linguistic style used to express rumors, characteristics of people involved in propagating information and structural features are studied. Structural features include features of reply tree and user graph. Structural features were extracted as new features in order to enhance the efficiency of the rumor conversations detection. These features provide valuable clues on how a source tweet is transmitted and responds over time. Experimental results indicate that the new features are effective in detecting rumors and that the proposed method is better than other methods as F1-score increased by 4%. Implementation of the proposed method was carried out on Twitter datasets collected during five breaking news stories.

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A semi-supervised model for Persian rumor verification based on content information

Cited by 18 publications

References 44 publications

Research status of deep learning methods for rumor detection

Research status of deep learning methods for rumor detection

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Rumor conversations detection in twitter through extraction of structural features

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