Hunting Malicious Bots on Twitter: An Unsupervised Approach

Chen, Zhouhan; Tanash, Rima S.; Stoll, Richard J.; Subramanian, Devika

doi:10.1007/978-3-319-67256-4_40

Cited by 22 publications

(8 citation statements)

References 8 publications

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“…Building on the previous research, we enhanced our list of URL shorteners, including the most common shorteners among bots (e.g., dlvr.it, dld.bz, viid.me, or ln.is). In the study by Chen et al (2017), which compiled a comprehensive list of this type of shortener, more than 10% of tweets including them would be generated by bots.…”

Section: Content Analysismentioning

confidence: 99%

The Role of Suspended Accounts in Political Discussion on Social Media: Analysis of the 2017 French, UK and German Elections

Majó-Vázquez

Congosto

Nicholls

et al. 2021

Social Media + Society

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Content moderation on social media is at the center of public and academic debate. In this study, we advance our understanding on which type of election-related content gets suspended by social media platforms. For this, we assess the behavior and content shared by suspended accounts during the most important elections in Europe in 2017 (in France, the United Kingdom, and Germany). We identify significant differences when we compare the behavior and content shared by Twitter suspended accounts with all other active accounts, including a focus on amplifying divisive issues like immigration and religion and systematic activities increasing the visibility of specific political figures (often but not always on the right). Our analysis suggests that suspended accounts were overwhelmingly human operated and no more likely than other accounts to share “fake news.” This study sheds light on the moderation policies of social media platforms, which have increasingly raised contentious debates, and equally importantly on the integrity and dynamics of political discussion on social media during major political events.

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Section: Content Analysismentioning

confidence: 99%

The Role of Suspended Accounts in Political Discussion on Social Media: Analysis of the 2017 French, UK and German Elections

Majó-Vázquez

Congosto

Nicholls

et al. 2021

Social Media + Society

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“…erefore, in this paper, the number of likes, comments, and reposts of a post is defined as λ nl , λ nc , and λ nr . (2) e originality of post: Many social bots generate their own posts by reposting or copying other users' posts, and thus the frequency of reposted posts of social bots is higher than that of normal users [46]. In this paper, we use λ or to define the originality of user post, and take 1 when the post is reposted from other users, otherwise, take 0.…”

Section: Semantic Feature Sequencementioning

confidence: 99%

A New Joint Approach with Temporal and Profile Information for Social Bot Detection

Yang

Chen

Wang

et al. 2022

Security and Communication Networks

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With the increasing popularity of online social networks (OSNs), a huge number of social bots have emerged. Social bots are involved in various cybercrimes like cyberbullying and rumor dissemination, which have seriously affected the normal order of OSNs. Nowadays, existing studies in this field almost focus on English OSNs like Twitter and Facebook. However, it is difficult to directly apply these detection technologies to Sina Weibo, which is one of the largest Chinese microblogging services in the world. In addition, social bots are evolving rapidly and time-consuming feature engineering may not perform well in detecting newly emerging social bots. In this paper, we propose a new joint approach with Temporal and Profile information for social bot detection (TPBot). The approach includes data collection module, feature extraction module, and detection module. To begin with, data collection module uses a web crawler to obtain user data from Sina Weibo. Next, the feature extraction module regards the user posts as temporal data to extract temporal-semantic and temporal-metadata features. Furthermore, this module extracts features based on users’ profile. Finally, a detection model based on BiGRU and attention mechanism is designed in the detection module. The results show that TPBot performs better than baselines with the F1-score of 0.9837 on the Sina Weibo dataset. Moreover, we have also conducted an experiment on the two datasets collected from Twitter to evaluate the generalization ability of TPBot. It is found that TPBot outperforms baselines on the new datasets and has good generalization ability.

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“…Over the years, many methods have been proposed to detect spam based on the content of the email. These methods fall into three main categories: supervised, semi-supervised [8], and unsupervised [9] methods. Supervised methods treat mail detection as a classification task, and supervised methods generally show better performance compared to other methods [10].…”

Section: Related Workmentioning

confidence: 99%

Chinese Spam Detection based on Prompt Tuning

Zhang¹,

An²

2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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Spam has plagued Internet users for a long time, and it is of great significance to design an efficient spam detection method. In recent years, spam detection methods based on fine-tuning pre-trained language models (PLM) have achieved great success. The approach is to fine-tune a pre-trained language model on a large dataset to adapt it to the downstream spam detection task. However, the objective of the initial training phase of PLM is inconsistent with the objective of downstream tasks, which results in the downstream tasks cannot fully utilize the latent knowledge in PLM. In this paper, we use Prompt Tuning and PLM to identify Chinese spam by constructing additional prompt templates, converting the email classification task into a fill-in-the-blank task, and then getting the email classification results according to the filling content on the prompt templates. This process is very similar to the process of initial training of PLM, which can more fully utilize the rich knowledge in PLM. We use prompt tuning to train the model on public datasets. Through experiments, we found that the accuracy score of the proposed model on trec06 datasets can reach 0.996, and the F1 score can reach 0.994, which is better than the comparison model. In terms of model convergence speed, the proposed model only needs less than 200 training steps to converge, which is faster than the comparison model.

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Hunting Malicious Bots on Twitter: An Unsupervised Approach

Cited by 22 publications

References 8 publications

The Role of Suspended Accounts in Political Discussion on Social Media: Analysis of the 2017 French, UK and German Elections

The Role of Suspended Accounts in Political Discussion on Social Media: Analysis of the 2017 French, UK and German Elections

A New Joint Approach with Temporal and Profile Information for Social Bot Detection

Chinese Spam Detection based on Prompt Tuning

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