Ant Colony Decision Tree Method to Detect the Suspicious News
Asha Kumari1, Balkishan2
1Asha Kumari, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.
2Balkishan, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 269-272 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6794118419/2019©BEIESP | DOI: 10.35940/ijrte.D6794.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The ease of availability and low cost of social media platforms has made the human vulnerable to devour the information from social media. Social media serves the unverified information which easily gets disseminated among people through different groups, applications, and other online social platforms. These fake news can lead to any suspicious activities which urges to derive the term suspicious news. The escalation of these suspicious news has open a lot of research ventures to detect and attenuate the impact on human. These suspicious activities have become a nuisance for legitimate users. Despite the presence of existing methods for the suspicious news detection exists, but the continuous growth of such activities is difficult to manage with an individual approach. In this research work, an ensemble approach is considered to detect suspicious news content. Here, Ant Colony Decision Tree method is ensembled for the detection of suspicious news (ACDTDSN). This approach uses the heuristic function and pheromone trail to obtain the optimal solution. The overall functionality of system is based on the content based approach for the detection of suspicious news with steps of dataset consideration, pre-processing, feature selection, and classification. The experimentation is performed using the FakenewNet dataset which consist of BuzzFeed and PolitiFact categories of news content. The results of the proposed ACSTDSN framework are accessed with the performance evaluation measures.
Keywords: Fake News, Suspicious News, Ant Colony Optimization, Decision Tree, Artificial Intelligence, Text Mining.
Scope of the Article: Text Mining.