کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6902440 | 1446500 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
HashMiner: Feature Characterisation and analysis of #Hashtag Hijacking using real-time neural network
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
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چکیده انگلیسی
Online social media has become a vital platform to discuss common topics which are being categorised under a single name: Hashtag where people put their views, opinions and data. Thus hashtags have become a victim for spam, fake and un-related advertising content dissemination. In this paper we propose a novel approach designed on 9 distinctive parameters which extends to 4 other derived statistic from Twitter Streaming API, to detect Hashtag hijacking using Neural network analysis which shows a mean hijacking percentage of 28.5 over 10, 240 test tweets collected whereas, manual based annotation performed results in 17.14 %hijacking.Our method over collected dataset results in 94.025% accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 115, 2017, Pages 786-793
Journal: Procedia Computer Science - Volume 115, 2017, Pages 786-793
نویسندگان
Deepali Virmani, Nikita Jain, Ketan Parikh, Abhishek Srivastava,