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首页> 外文期刊>Procedia Computer Science >Detecting and Characterizing Arab Spammers Campaigns in Twitter
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Detecting and Characterizing Arab Spammers Campaigns in Twitter

机译：在Twitter中检测和表征阿拉伯垃圾邮件发送者活动

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Social media platforms play a significant role in today’s society as billions of users use them to share or seek information on a regular basis. The content in social media has a significant impact that influences individuals’ opinions and decision, which can range from buying a small product to voting for a political campaign. This role has been fueling the interest in utilizing social media content for research and commercial purposes. The significant role of social media also attracted other individuals who intentionally abuse or misuse these platforms by producing and managing a tremendous amount of fake accounts to perform various malicious activities. Their activities are often center on sharing unwanted, misleading, or harmful content to either send unwanted ads, manipulate public opinion, or spread harmful malware. This act would eventually reduce the quality of social media content as a mean of information source used for various purposes. Therefore, this research proposes a machine-learning based model that aims to detect malicious users and groups on Twitter. We have, in particular, focus on malicious Arab accounts as it has not been sufficiently researched. The proposed model adopts a semi-supervised technique that labels Twitter accounts based on their behavior and profile information into spam or genuine account. To evaluate this technique, we collected a dataset through a Twitter API by targeting active Arab users. We manually labeled approximately 500 accounts as the ground truth, and then, we developed a classification model with a set of redefined features with the aim of identifying individual and groups of spam accounts. We have also evaluated the performance of the proposed model, and the results show that our model achieves 0.89 F measure: 0.89 and 0.91 Accuracy.
机译：社交媒体平台在当今社会中发挥着重要作用，数十亿用户定期使用社交媒体平台共享或查找信息。社交媒体中的内容具有很大的影响力，影响着个人的意见和决定，其范围从购买小产品到为政治运动投票。这个角色一直在激发人们将社交媒体内容用于研究和商业目的的兴趣。社交媒体的重要作用还吸引了其他人，这些人通过产生和管理大量假账户来执行各种恶意活动，有意滥用或滥用这些平台。他们的活动通常集中在共享不需要的，误导性或有害的内容，以发送不需要的广告，操纵公众舆论或传播有害的恶意软件。该法案最终将降低社交媒体内容的质量，作为用于各种目的的信息源的手段。因此，本研究提出了一种基于机器学习的模型，旨在检测Twitter上的恶意用户和组。由于没有足够的研究，我们特别关注恶意的阿拉伯帐户。提议的模型采用半监督技术，该技术基于Twitter帐户的行为和配置文件信息将其标记为垃圾邮件或真实帐户。为了评估这种技术，我们通过针对活跃的阿拉伯用户的Twitter API收集了一个数据集。我们手动将大约500个帐户标记为基本事实，然后，我们开发了具有一系列重新定义功能的分类模型，目的是识别垃圾邮件帐户的个人和组。我们还评估了所提出模型的性能，结果表明我们的模型达到了0.89 F的测量值：0.89和0.91精度。

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