An Online Water Army Detection Method Based on Network Hot Events

Zhang, Weisha; Lu, Jiazhong

doi:10.1109/icmtma.2018.00053

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…With respect to the NB, the model proposed by Zheng et al [49] had the highest Accuracy, which is 89.49%. The Precision of the proposed model by using NN is 74.11% in case 1, which is respectively 3%, 1.33%, 3.07%, and 1.68% higher than the models proposed by Tian et al [48], Zheng et al [49], Dai and Wang [50], and Zhang and Lu [22]. In addition, the proposed model performed best regarding the Recall by using all three machining learning algorithms in case 1, which is 75.86%, 75.39% and 47.53%, respectively.…”

Section: B Resultsmentioning

confidence: 65%

“…In the present paper, three commonly applied machine learning methods are used to train the classifiers, namely the Neural network (NN), Naive Bayes (NB), and Support Vector Machine (SVM). The reason for employing these four classical methods is that they have shown great performance in many recent papers focusing on the online water army detection [22], [36]. In addition, as the main contributions of this paper were to introduce the supernetwork theory into water army detection problem and evaluate its effects on detection performance, using classic classification algorithms could provide a more fairly assessment, to a large extent.…”

Section: B Resultsmentioning

confidence: 99%

“…Moreover, to provide a comparison analysis, we also selected four existing Internet water army detection models as the baseline, i.e., those proposed by Tian et al [48], Zheng et al [49], Dai and Wang [50], and Zhang and Lu [22]. These four models were chosen because the datasets employed by them are all from the Weibo website, and thus the negative effects caused by language differences can be avoided.…”

Section: B Resultsmentioning

confidence: 99%

“…To accurately reconstruct the features proposed by these papers, we strictly followed the steps shown in their papers and employed one of the most popular word segmentation software, ICTCLAS, and its embedded lexicon. It should be noted that, due to Zhang and Lu [22] did not describe the clustering method in their work, the K-means algorithm is applied to calculate the similarity of posts in this paper. Moreover, according to previous practice [21], [40], [51], the performance of the Internet water army detection model was evaluated based on four metrics, namely Accuracy, Precision, Recall, and F1-score, which are obtained through a confusion matrix.…”

Section: B Resultsmentioning

confidence: 99%

“…Much evidence has confirmed the negative influences of Internet water armies on the order of Internet and even the society as a whole [1], [2], [21], [22]. For instance, as Zhang and Lu [22] stated that, due to the rumors and false information posted by Internet water armies, the normal network order and social harmony and stability have been seriously disturbed. Thus, effectively identifying the spammers or Internet water armies in social Web sites is of great significance.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

An Internet Water Army Detection Supernetwork Model

et al. 2019

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The emergence of Internet water armies has strongly affected the information quality of online communication platforms, thus disrupting the order of the Internet. Accurate detection of Internet water armies is therefore of great significance. Based on the supernetwork theory, a new Internet water army detection model is proposed in this paper, in which a supernetwork with four layers is established, including social subnetwork, information subnetwork, psychological subnetwork, and negative keyword subnetwork. Then, personal information of users, dissemination process of information, the transformation process of different psychologies, the similarity between different keywords, and the connections between different subnetworks are considered in the model. Thus, nine composite indexes are proposed, the majority of which are used for the first time in detecting Internet water armies. A dataset selected from the largest online communication platform in China, the Weibo website, is used to test the performance of the model. Four existing water army detection models introduced in previous studies are used to provide a comparison analysis. The results show that our proposed model has better performance in terms of accuracy and stability than the other four existing models, which thanks to the employment of the supernetwork theory. We believe that our proposed model could be helpful for information researchers to further understand the complex nature of Internet water armies, as well as for the government to better manage the Internet.

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Section: B Resultsmentioning

confidence: 65%

Section: B Resultsmentioning

confidence: 99%

Section: B Resultsmentioning

confidence: 99%

Section: B Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

An Internet Water Army Detection Supernetwork Model

et al. 2019

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Research on Hot Spot Mining Technology for Network Public Opinion

Xie

Han

et al. 2022

Data Mining and Big Data

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Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

et al. 2020

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This systematic mapping literature aims to identify current research and directions for future studies in terms of combating cyber propaganda in the social media, which is used by both human effort and technological approaches (socio-technical) for mitigation. Out of 5176 retrieved articles, only 98 of them were selected for primary studies; classified based on research artifacts, mitigation effort, and the social media platforms involved in the research. The search was conducted using selected databases and applying selection criteria set for this research. Through the analysis, important research trends were identified based on human effort and technological approaches in mitigating and combating the cyber-propaganda issues. The authors also identified various mitigation socio-technical approaches such as identification, detection, image recognition, prediction, truth discovery and comprehension of rumours flow. The study also highlights areas for further improvements, to complement the performances of existing techniques. Besides, the study provides a brief review of cyber propaganda detection using classification techniques. Hence, it has set forth applicable research focus on the areas dealing with the mitigation of risk borne by cyber propaganda in the social media. INDEX TERMS Systematic mapping review, social media, cyber propaganda, mitigation, human effort, socio-technical approaches.

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An Online Water Army Detection Method Based on Network Hot Events

Cited by 7 publications

References 6 publications

An Internet Water Army Detection Supernetwork Model

An Internet Water Army Detection Supernetwork Model

Research on Hot Spot Mining Technology for Network Public Opinion

Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

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