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Title:	社群媒體異常帳號探勘系統的設計與實作 Design and Implementation of the Intelligent Discovery System to Explore Malicious Accounts over Social Media
Authors:	蕭君弘 Hsiao, Chun-Hung
Contributors:	沈錳坤 Shan, Man-Kwan 蕭君弘 Hsiao, Chun-Hung
Keywords:	異常帳號智慧型探勘系統以例探索 Malicious Account Intelligent Discovery System Explore By Examples
Date:	2020
Issue Date:	2020-09-02 13:15:45 (UTC+8)
Abstract:	近年來社群媒體越來越興盛，改變了大眾接收資訊的習慣。社群媒體上的話語權也成了不同陣營攻防的重點。因此社群媒體上開始出現越來越多行為可疑的異常帳號，試圖操弄輿論引導風向，藉此獲得自身的利益。本論文研究開發一個社群媒體上的異常帳號探勘分析，透過發文及留言互動行為探索分析異常帳號。本系統的核心精神為由已知的異常帳號找出其他未知異常帳號。系統提供兩大主要功能Explore By Examples及Analysis By Examples。前者著重在探索異常帳號、後者著重在分析異常行為。兩大功能都由作息、來源IP、回應行為、網絡四個面向來探索分析，以協助使用者探索出異常帳號。本論文以台灣最熱門的本土社群媒體平台PTT BBS資料進行實證，並根據PTT官方公告異常帳號運用本系統進行案例分析。 In recent years, with the growth of social media, public`s habits of receiving information have been changed. More and more malicious accounts appear on social media. These malicious accounts try to manipulate public opinion for spin control to gain their own interests. This thesis aims at the design and implementation of an intelligent discovery system to explore malicious accounts over social media. The core spirit of the developed system lies in the exploration of unknown malicious accounts by known malicious accounts over social media. Two main functions provided by the developed system are exploration by examples and analysis by examples. While the former focuses on exploration of unknown malicious accounts, the latter focuses on the analysis of known ones. To explore and analyze malicious account, four aspects of malicious behaviors are considered, namely activity, IP address, like/dislike behaviors, and network. The developed system is verified on the data collected from PTT bulletin board system, which is the most popular domestic social media service in Taiwan. Four case studies are performed to demonstrate the superiority of the developed intelligent discovery system.
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Description:	碩士國立政治大學資訊科學系碩士在職專班 107971020
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107971020
Data Type:	thesis
DOI:	10.6814/NCCU202001683
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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