The Experts below are selected from a list of 1158 Experts worldwide ranked by ideXlab platform
Aihong Qin - One of the best experts on this subject based on the ideXlab platform.
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constrained nmf based semi supervised learning for social media Spammer detection
Knowledge Based Systems, 2017Co-Authors: Nan Chen, Frank Jiang, Aihong QinAbstract:Within the past few years, social media platforms such as Facebook, Twitter, and Sina Weibo, have gradually become important channels for information dissemination and communication. However, in the meantime, these platforms are prone to be potentially attacked by Spammers, who usually propagate disgusted information such as phishing URLs, false news, and even pornography to other users. Despite rapid increase of social media Spammers, the traditional Spammer detection methods become less effective. In this paper, we present a novel semi-supervised social media Spammer detection approach, making full use of the message content and user behavior as well as the social relation information. First, we adapt the original constrained NMF-based semi-supervised learning (CNMF) algorithm, nonnegative matrix factorization (NMF) by imposing a label information constrain and sparseness constrain. Second, we present a novel CNMF-based integral framework for social media Spammer detection by implementing the collaborative factorization on the message content matrix and the user behavior and social relation information matrix. Moreover, we explore the iterative update rule (IUR) and optimization algorithm for the Spammer detection model. In addition, its corresponding convergence is also proven. Extensive experiments are conducted on the real-world dataset from Sina Weibo, the experiment results demonstrate that our proposed model performs significantly better than the conventionally applied supervised classifiers for the Spammer detection.
Huan Liu - One of the best experts on this subject based on the ideXlab platform.
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social Spammer detection with sentiment information
International Conference on Data Mining, 2014Co-Authors: Jiliang Tang, Huiji Gao, Huan LiuAbstract:Social media is a popular platform for Spammers to unfairly overwhelm normal users with unwanted or fake content via social networking. The Spammers significantly hinder the use of social media systems for effective information dissemination and sharing. Different from the Spammers in traditional platforms such as email and the Web, Spammers in social media can easily connect with each other, sometimes without mutual consent. They collude with each other to imitate normal users by quickly accumulating a large number of "human" friends. In addition, content information in social media is noisy and unstructured. It is infeasible to directly apply traditional Spammer detection methods in social media. Understanding and detecting deception has been extensively studied in traditional sociology and social sciences. Motivated by psychological findings in physical world, we investigate whether sentiment analysis can help Spammer detection in online social media. In particular, we first conduct an exploratory study to analyze the sentiment differences between Spammers and normal users, and then present an optimization formulation that incorporates sentiment information into a novel social Spammer detection framework. Experimental results on real-world social media datasets show the superior performance of the proposed framework by harnessing sentiment analysis for social Spammer detection.
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online social Spammer detection
National Conference on Artificial Intelligence, 2014Co-Authors: Jiliang Tang, Huan LiuAbstract:The explosive use of social media also makes it a popular platform for malicious users, known as social Spammers, to overwhelm normal users with unwanted content. One effective way for social Spammer detection is to build a classifier based on content and social network information. However, social Spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social Spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social Spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social Spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social Spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.
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leveraging knowledge across media for Spammer detection in microblogging
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014Co-Authors: Jiliang Tang, Huan LiuAbstract:While microblogging has emerged as an important information sharing and communication platform, it has also become a convenient venue for Spammers to overwhelm other users with unwanted content. Currently, Spammer detection in microblogging focuses on using social networking information, but little on content analysis due to the distinct nature of microblogging messages. First, label information is hard to obtain. Second, the texts in microblogging are short and noisy. As we know, Spammer detection has been extensively studied for years in various media, e.g., emails, SMS and the web. Motivated by abundant resources available in the other media, we investigate whether we can take advantage of the existing resources for Spammer detection in microblogging. While people accept that texts in microblogging are different from those in other media, there is no quantitative analysis to show how different they are. In this paper, we first perform a comprehensive linguistic study to compare spam across different media. Inspired by the findings, we present an optimization formulation that enables the design of Spammer detection in microblogging using knowledge from external media. We conduct experiments on real-world Twitter datasets to verify (1) whether email, SMS and web spam resources help and (2) how different media help for Spammer detection in microblogging.
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social Spammer detection in microblogging
International Joint Conference on Artificial Intelligence, 2013Co-Authors: Jiliang Tang, Yanchao Zhang, Huan LiuAbstract:The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for Spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social Spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for Spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social Spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social Spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social Spammer detection.
Nan Chen - One of the best experts on this subject based on the ideXlab platform.
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constrained nmf based semi supervised learning for social media Spammer detection
Knowledge Based Systems, 2017Co-Authors: Nan Chen, Frank Jiang, Aihong QinAbstract:Within the past few years, social media platforms such as Facebook, Twitter, and Sina Weibo, have gradually become important channels for information dissemination and communication. However, in the meantime, these platforms are prone to be potentially attacked by Spammers, who usually propagate disgusted information such as phishing URLs, false news, and even pornography to other users. Despite rapid increase of social media Spammers, the traditional Spammer detection methods become less effective. In this paper, we present a novel semi-supervised social media Spammer detection approach, making full use of the message content and user behavior as well as the social relation information. First, we adapt the original constrained NMF-based semi-supervised learning (CNMF) algorithm, nonnegative matrix factorization (NMF) by imposing a label information constrain and sparseness constrain. Second, we present a novel CNMF-based integral framework for social media Spammer detection by implementing the collaborative factorization on the message content matrix and the user behavior and social relation information matrix. Moreover, we explore the iterative update rule (IUR) and optimization algorithm for the Spammer detection model. In addition, its corresponding convergence is also proven. Extensive experiments are conducted on the real-world dataset from Sina Weibo, the experiment results demonstrate that our proposed model performs significantly better than the conventionally applied supervised classifiers for the Spammer detection.
Jiliang Tang - One of the best experts on this subject based on the ideXlab platform.
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social Spammer detection with sentiment information
International Conference on Data Mining, 2014Co-Authors: Jiliang Tang, Huiji Gao, Huan LiuAbstract:Social media is a popular platform for Spammers to unfairly overwhelm normal users with unwanted or fake content via social networking. The Spammers significantly hinder the use of social media systems for effective information dissemination and sharing. Different from the Spammers in traditional platforms such as email and the Web, Spammers in social media can easily connect with each other, sometimes without mutual consent. They collude with each other to imitate normal users by quickly accumulating a large number of "human" friends. In addition, content information in social media is noisy and unstructured. It is infeasible to directly apply traditional Spammer detection methods in social media. Understanding and detecting deception has been extensively studied in traditional sociology and social sciences. Motivated by psychological findings in physical world, we investigate whether sentiment analysis can help Spammer detection in online social media. In particular, we first conduct an exploratory study to analyze the sentiment differences between Spammers and normal users, and then present an optimization formulation that incorporates sentiment information into a novel social Spammer detection framework. Experimental results on real-world social media datasets show the superior performance of the proposed framework by harnessing sentiment analysis for social Spammer detection.
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online social Spammer detection
National Conference on Artificial Intelligence, 2014Co-Authors: Jiliang Tang, Huan LiuAbstract:The explosive use of social media also makes it a popular platform for malicious users, known as social Spammers, to overwhelm normal users with unwanted content. One effective way for social Spammer detection is to build a classifier based on content and social network information. However, social Spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social Spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social Spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social Spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social Spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.
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leveraging knowledge across media for Spammer detection in microblogging
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014Co-Authors: Jiliang Tang, Huan LiuAbstract:While microblogging has emerged as an important information sharing and communication platform, it has also become a convenient venue for Spammers to overwhelm other users with unwanted content. Currently, Spammer detection in microblogging focuses on using social networking information, but little on content analysis due to the distinct nature of microblogging messages. First, label information is hard to obtain. Second, the texts in microblogging are short and noisy. As we know, Spammer detection has been extensively studied for years in various media, e.g., emails, SMS and the web. Motivated by abundant resources available in the other media, we investigate whether we can take advantage of the existing resources for Spammer detection in microblogging. While people accept that texts in microblogging are different from those in other media, there is no quantitative analysis to show how different they are. In this paper, we first perform a comprehensive linguistic study to compare spam across different media. Inspired by the findings, we present an optimization formulation that enables the design of Spammer detection in microblogging using knowledge from external media. We conduct experiments on real-world Twitter datasets to verify (1) whether email, SMS and web spam resources help and (2) how different media help for Spammer detection in microblogging.
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social Spammer detection in microblogging
International Joint Conference on Artificial Intelligence, 2013Co-Authors: Jiliang Tang, Yanchao Zhang, Huan LiuAbstract:The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for Spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social Spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for Spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social Spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social Spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social Spammer detection.
Arif Mudi Priyatno - One of the best experts on this subject based on the ideXlab platform.
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Spammer detection based on account tweet and community activity on twitter
Jurnal Ilmu Komputer dan Informasi, 2020Co-Authors: Arif Mudi PriyatnoAbstract:Spammers are the activities of users who abuse Twitter to spread spam. Spammers imitate legitimate user behavior patterns to avoid being detected by spam detectors. Spammers create lots of fake accounts and collaborate with each other to form communities. The collaboration makes it difficult to detect Spammers' accounts. This research proposed the development of feature extraction based on hashtags and community activities for the detection of Spammer accounts on Twitter. Hashtags are used by Spammers to increase popularity. Community activities are used as features for the detection of Spammers so as to give weight to the activities of Spammers contained in a community. The experimental result shows that the proposed method got the best performance in accuracy, recall, precision and g-means with are 90.55%, 88.04%, 3.18%, and 16.74%, respectively. The accuracy and g-mean of the proposed method can surpassed previous method with 4.23% and 14.43%. This shows that the proposed method can overcome the problem of detecting Spammer on Twitter with better performance compared to state of the art.
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deteksi bot Spammer twitter berbasis time interval entropy dan global vectors for word representations tweet s hashtag
Register: Jurnal Ilmiah Teknologi Sistem Informasi, 2019Co-Authors: Arif Mudi Priyatno, Muhammad Mirza Muttaqi, Fahmi Syuhada, Agus Zainal ArifinAbstract:Bot Spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user . Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot Spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet . Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall , precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot Spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot Spammer . Spam Spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot Spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot Spammers.
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Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag
Universitas Pesantren Tinggi Darul Ulum (Unipdu) Jombang, 2019Co-Authors: Arif Mudi Priyatno, Muhammad Mirza Muttaqi, Fahmi Syuhada, Agus Zainal ArifinAbstract:Bot Spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot Spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot Spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot Spammer. Spam Spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot Spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot Spammers