本文選題：社交網(wǎng)絡(luò)　切入點：垃圾信息檢測　出處：《山東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)如今,隨著Web2.0的發(fā)展,社交網(wǎng)絡(luò)在人們的生活中扮演了越來越重要的角色,如今主流的社交網(wǎng)絡(luò)平臺有新浪微博、百度知道、微信、QQ、一直播、知乎、豆瓣等,同時,隨著手機等通訊工具的普及,為人們隨時隨地通過網(wǎng)絡(luò)進(jìn)行網(wǎng)上閱覽、分享信息、互動提供了便利。然而,正是這種便利性,催生了一大批垃圾用戶,這些用戶在平臺上發(fā)布惡意鏈接、推廣虛假廣告、恣意中傷他人、傳播各類謠言等等,嚴(yán)重影響了用戶體驗,給人們的生活帶來了困擾,其消極影響愈加顯著。因此,如何識別并檢測這些惡意用戶、屏蔽垃圾內(nèi)容,成為當(dāng)下研究的熱點問題。本文選取了時下主流的社交平臺——新浪微博,以及知識共享平臺——百度知道,利用機器學(xué)習(xí)技術(shù)以及排序思想分別對兩個平臺的垃圾信息進(jìn)行處理,設(shè)計了針對微博的垃圾信息檢測算法以及針對百度知道的隱性垃圾答案沉降算法。本文的主要內(nèi)容如下:首先,介紹了社交網(wǎng)絡(luò)的定義發(fā)展以及常見的網(wǎng)絡(luò)中的垃圾信息問題,分別針對微博和問答網(wǎng)站進(jìn)行了垃圾問題概述,包括垃圾信息的分類、處理技術(shù)。其次,針對微博中的垃圾信息,提出了基于顏色的可視化垃圾行為特征提取和基于詞項黑名單的垃圾內(nèi)容特征提取,同時,在這兩種特征集合的基礎(chǔ)上,提出了基于貝葉斯網(wǎng)絡(luò)的垃圾信息檢測算法。實驗證明,基于貝葉斯網(wǎng)絡(luò)的垃圾信息處理算法分類結(jié)果要優(yōu)于樸素貝葉斯算法,同時優(yōu)于分別針對垃圾行為和垃圾內(nèi)容檢測的算法。最后,針對問答網(wǎng)站中的垃圾信息,先將垃圾答案分為顯性和隱性,對較難用技術(shù)手段分類的隱性垃圾答案提出了沉降算法,引用物理學(xué)物體下落的思想,結(jié)果證明,該算法能夠有效地將垃圾答案沉到答案序列的底端。
[Abstract]:Nowadays, with the development of Web2.0, social networks play a more and more important role in people's life. Nowadays, the mainstream social network platforms include Sina Weibo, Baidu knows, WeChat QQQ1, live broadcast, Zhihu, Douban, etc. At the same time, With the popularity of mobile phone and other communication tools, it is convenient for people to read online, share information and interact with each other through the Internet anytime and anywhere. However, it is this kind of convenience that has given birth to a large number of junk users. These users issue malicious links on the platform, promote false advertisements, wanton slander others, spread rumors and so on, seriously affect the user experience, bring troubles to people's lives, and its negative effects become more and more significant. How to identify and detect these malicious users and block spam content has become a hot topic of current research. This paper selects Weibo, a popular social platform, and Baidu, a knowledge-sharing platform, to know. Using machine learning technology and sorting thought to deal with the garbage information of the two platforms, The main contents of this paper are as follows: firstly, the definition and development of social network and the common problems of garbage information in the network are introduced. For Weibo and Q & A websites, respectively, the garbage problem was summarized, including the classification and processing techniques of garbage information. Secondly, aiming at the garbage information in Weibo, In this paper, color based visual garbage behavior feature extraction and word term blacklist based garbage content feature extraction are proposed. At the same time, on the basis of these two feature sets, The algorithm of spam detection based on Bayesian network is proposed. The experimental results show that the classification result of the algorithm based on Bayesian network is better than that of naive Bayesian algorithm. At the same time, it is better than the algorithms for spam behavior and spam content detection, respectively. Finally, according to the spam information in the question and answer website, the garbage answers are classified as explicit and implicit first. A settlement algorithm is proposed for recessive garbage answers which are difficult to classify by technical means, and the idea of physical objects falling is cited. The results show that the algorithm can effectively sink the garbage answers to the bottom of the answer sequence.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.092;TP301.6

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