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Title: :  Rumor Detection In Social Media
PaperId: :  10254
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 5 Issue 3 2019
DUI:    16.0415/IJARIIE-10254
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Karen JoyK.K.Wagh Institute of Engineering Education and Research
Sumesh MeppadathK.K.Wagh Institute of Engineering Education and Research
Kaustubh KansaraK.K.Wagh Institute of Engineering Education and Research
Nishant ShrivastavK.K.Wagh Institute of Engineering Education and Research

Abstract

Computer Engineering
Rumor Detection,Twitter, Twitter API,Social Media
Social Media has outpaced the conventional news media systems. It is often observed that news stories are first broken in cyber space and then the electronic and print media take them up. However, the distributed architecture and lack of moderation in most of the social media platforms, with the temptation of the users for posting a newsworthy story early on, makes the veracity of information a major issue. This information is defined as rumour, which is a non-credible piece of data circulating in cyberspace, often causing social unrest. For establishing credibility of this information, we define a two phase approach, considering Twitter as the target social networking platform specifically of Indian domain. First phase is based on the premise that verified News Channel Handles in Twitter would furnish more credible information as compared to the nave general public at large. Live streaming or recent tweets are extracted corresponding to Twitter trends, based on clustering using Hashtags. Contextual and sentiment mismatch ratio between the tweets of above mentioned classes is done using semantic and sentiment analysis of tweets,which would reflect the degree of discrepancy of the information. However, as a tweet is restricted to 140 characters, it may not be sufficient to gain useful insight. Tweets are also is susceptible to noise, which may decrease the accuracy of analysis. To overcome these problems, the second phase verifies the claim credibility result, from heterogeneous data resources like authentic web articles, web pages, blog posts, websites, etc. using web crawling techniques. The performance of the proposed method is evaluated, considering the recent examples of popular rumours that went viral in social media.

Citations

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IJARIIE Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav. "Rumor Detection In Social Media" International Journal Of Advance Research And Innovative Ideas In Education Volume 5 Issue 3 2019 Page 351-355
MLA Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav. "Rumor Detection In Social Media." International Journal Of Advance Research And Innovative Ideas In Education 5.3(2019) : 351-355.
APA Karen Joy, Sumesh Meppadath, Kaustubh Kansara, & Nishant Shrivastav. (2019). Rumor Detection In Social Media. International Journal Of Advance Research And Innovative Ideas In Education, 5(3), 351-355.
Chicago Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav. "Rumor Detection In Social Media." International Journal Of Advance Research And Innovative Ideas In Education 5, no. 3 (2019) : 351-355.
Oxford Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav. 'Rumor Detection In Social Media', International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 3, 2019, p. 351-355. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Rumor_Detection_In_Social_Media_ijariie10254.pdf (Accessed : 29 June 2022).
Harvard Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav. (2019) 'Rumor Detection In Social Media', International Journal Of Advance Research And Innovative Ideas In Education, 5(3), pp. 351-355IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Rumor_Detection_In_Social_Media_ijariie10254.pdf (Accessed : 29 June 2022)
IEEE Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav, "Rumor Detection In Social Media," International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 3, pp. 351-355, May-Jun 2019. [Online]. Available: http://ijariie.com/AdminUploadPdf/Rumor_Detection_In_Social_Media_ijariie10254.pdf [Accessed : 29 June 2022].
Turabian Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav. "Rumor Detection In Social Media." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 5 number 3 (29 June 2022).
Vancouver Karen Joy, Sumesh Meppadath, Kaustubh Kansara, and Nishant Shrivastav. Rumor Detection In Social Media. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2019 [Cited : 29 June 2022]; 5(3) : 351-355. Available from: http://ijariie.com/AdminUploadPdf/Rumor_Detection_In_Social_Media_ijariie10254.pdf
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