| Title | Analysis of Twitter Spam Detection Using Machine Learning Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Das, Lipsa, Ahuja, Laxmi, Pandey, Adesh |
| Conference Name | 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) |
| Keywords | Blogs, Filtering, Human Behavior, Internet, Metrics, OSN spam, phishing, pubcrawl, Scalability, Servers, social networking (online), spam detection, spam detection Twitter spam, spam filtering, Support vector machines |
| Abstract | Now a days there are many online social networks (OSN) which are very popular among Internet users and use this platform for finding new connections, sharing their activities and thoughts. Twitter is such social media platforms which is very popular among this users. Survey says, it has more than 310 million monthly users who are very active and post around 500+ million tweets in a day and this attracts, the spammer or cyber-criminal to misuse this platform for their malicious benefits. Product advertisement, phishing true users, pornography propagation, stealing the trending news, sharing malicious link to get the victims for making money are the common example of the activities of spammers. In Aug-2014, Twitter made public that 8.5% of its active Twitter users (monthly) that is approx. 23+ million users, who have automatically contacted their servers for regular updates. Thus for a spam free environment in twitter, it is greatly required to detect and filter these spammer from the legitimate users. Here in our research paper, effectiveness & features of twitter spam detection, various methods are summarized with their benefits and limitations are presented. [1] |
| DOI | 10.1109/ICIEM54221.2022.9853100 |
| Citation Key | das_analysis_2022 |
Analysis of Twitter Spam Detection Using Machine Learning Approach