HSpam14

Sedhai, Surendra; Sun, Aixin

doi:10.1145/2766462.2767701

Cited by 65 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We combine MinHash clustering and incremental clustering to group tweets collected by keywords TC within a time window into clusters. It has been reported that the MinHash algorithm is effective in grouping near‐duplicate tweets (Sedhai & Sun, ). However, tweets with a different minimum hash value could also be similar.…”

Section: Proposed Method: Almikmentioning

confidence: 99%

Collecting event‐related tweets from twitter stream

Zheng

Sun

2018

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

Twitter provides a channel of collecting and publishing instant information on major events like natural disasters. However, information flow on Twitter is of great volume. For a specific event, messages collected from the Twitter Stream based on either location constraint or predefined keywords would contain a lot of noise. In this article, we propose a method to achieve both high‐precision and high‐recall in collecting event‐related tweets. Our method involves an automatic keyword generation component, and an event‐related tweet identification component. For keyword generation, we consider three properties of candidate keywords, namely relevance, coverage, and evolvement. The keyword updating mechanism enables our method to track the main topics of tweets along event development. To minimize annotation effort in identifying event‐related tweets, we adopt active learning and incorporate multiple‐instance learning which assigns labels to bags instead of instances (that is, individual tweets). Through experiments on two real‐world events, we demonstrate the superiority of our method against state‐of‐the‐art alternatives.

show abstract

Section: Proposed Method: Almikmentioning

confidence: 99%

Collecting event‐related tweets from twitter stream

Zheng

Sun

2018

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Before conducting analysis on the collection of tweets, we briefly describe the dataset and the data annotation process. A detailed explanation and discussion related to the annotation process were reported in our earlier work (Sedhai & Sun, ).…”

Section: Overview Of Hspam14mentioning

confidence: 99%

“…The collected tweets contain 20.21 million hashtags and 6.97 million hyperlinks. Among the collected tweets, 14.07 million tweets are in English and were labeled spam and ham to generate the HSpam14 dataset, as discussed previously (Sedhai & Sun, ).…”

Section: Overview Of Hspam14mentioning

confidence: 99%

“…In this section we briefly discuss the tweet annotation process as reported in Sedhai and Sun (). First, such as other studies (Grier et al, ), we labeled tweets about quick money gain and adult content as spam tweets.…”

Section: Overview Of Hspam14mentioning

confidence: 99%

“…In this paper, we report a tweet‐level analysis based on hashtags and tweet content, and a user‐level analysis based on user profiles. Our analysis is based on the HSpam14 dataset consisting of 14 million tweets with spam or ham labels (Sedhai & Sun, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An analysis of 14 Million tweets on hashtag‐oriented spamming*

Sedhai

Sun

2017

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

Over the years, Twitter has become a popular platform for information dissemination and information gathering. However, the popularity of Twitter has attracted not only legitimate users but also spammers who exploit social graphs, popular keywords, and hashtags for malicious purposes. In this paper, we present a detailed analysis of the HSpam14 dataset, which contains 14 million tweets with spam and ham (i.e., nonspam) labels, to understand spamming activities on Twitter. The primary focus of this paper is to analyze various aspects of spam on Twitter based on hashtags, tweet contents, and user profiles, which are useful for both tweet‐level and user‐level spam detection. First, we compare the usage of hashtags in spam and ham tweets based on frequency, position, orthography, and co‐occurrence. Second, for content‐based analysis, we analyze the variations in word usage, metadata, and near‐duplicate tweets. Third, for user‐based analysis, we investigate user profile information. In our study, we validate that spammers use popular hashtags to promote their tweets. We also observe differences in the usage of words in spam and ham tweets. Spam tweets are more likely to be emphasized using exclamation points and capitalized words. Furthermore, we observe that spammers use multiple accounts to post near‐duplicate tweets to promote their services and products. Unlike spammers, legitimate users are likely to provide more information such as their locations and personal descriptions in their profiles. In summary, this study presents a comprehensive analysis of hashtags, tweet contents, and user profiles in Twitter spamming.

show abstract

A Hybrid Approach for Detecting Spammers in Online Social Networks

Alghamdi

Watson

2018

Web Information Systems Engineering – WISE 2018

View full text Add to dashboard Cite

HSpam14

Cited by 65 publications

References 27 publications

Collecting event‐related tweets from twitter stream

Collecting event‐related tweets from twitter stream

An analysis of 14 Million tweets on hashtag‐oriented spamming*

A Hybrid Approach for Detecting Spammers in Online Social Networks

Contact Info

Product

Resources

About