【摘要】：微博消息真?zhèn)位祀s，，虛假消息傳播速度快、影響范圍廣，對社會(huì)、企業(yè)或個(gè)人能造成極大的不良影響。人為判斷消息的真?zhèn)卧跁r(shí)效性和準(zhǔn)確性上存在很大挑戰(zhàn)，因而構(gòu)建智能的、有效的識(shí)別虛假消息的機(jī)制,評估消息可信度成為一個(gè)非常迫切而重要的問題。本文以主流微博上的消息和用戶為研究對象，結(jié)合自然語言處理、統(tǒng)計(jì)學(xué)習(xí)以及行為分析等理論構(gòu)建模型。本文的研究工作主要體現(xiàn)在以下兩個(gè)方面： 第一，微博水軍識(shí)別研究。微博用戶在微博平臺(tái)中起主導(dǎo)作用，識(shí)別微博水軍是評估消息可信度的重要過程，目前識(shí)別微博水軍多為人工識(shí)別，難以在大數(shù)據(jù)中達(dá)到理想效果。本文通過分析微博中普通用戶和水軍的屬性和行為差異，定義識(shí)別水軍的有效特征，對特征的準(zhǔn)確性進(jìn)行了實(shí)驗(yàn)性對比，利用概率圖模型的思想，構(gòu)建了自動(dòng)識(shí)別微博水軍的WGM模型。將國內(nèi)外最具代表性的兩個(gè)微博平臺(tái)新浪微博和Twitter數(shù)據(jù)作為實(shí)驗(yàn)樣本，進(jìn)行大量對比實(shí)驗(yàn)，結(jié)果顯示利用提出的特征可以有效的識(shí)別微博平臺(tái)中的水軍，且WGM模型比傳統(tǒng)的機(jī)器學(xué)習(xí)方法更準(zhǔn)確、有效。 第二，微博謠言檢測與消息可信度研究。評估微博消息可信度，對消息真?zhèn)巫龀雠袆e是一個(gè)非常重要但困難的問題。本文從微博評論出發(fā)，定義支持性、內(nèi)容相關(guān)性、置信度三個(gè)特征衡量評論在謠言與真實(shí)微博中的差異性，利用評論特征構(gòu)建BPCM模型評估消息可信度，判別消息真?zhèn)�。采用新浪微博真�?shí)數(shù)據(jù)對特征與模型進(jìn)行詳細(xì)對比分析，結(jié)果表明利用微博評論可以有效的評估消息可信度，判別消息真?zhèn)�，特征對謠言與真實(shí)微博的評論區(qū)分度較好，BPCM模型可以有效地解決消息可信度的評估問題以及謠言檢測問題。 本課題的研究內(nèi)容有助于國家輿情監(jiān)控、企業(yè)營銷分析以及領(lǐng)域事件分析和精準(zhǔn)搜索。
[Abstract]:Weibo news is mixed, the false news spreads quickly, the influence scope is wide, can cause the great adverse influence to the society, the enterprise or the individual. There are great challenges in judging the validity and accuracy of messages, so it is an urgent and important problem to construct an intelligent and effective mechanism to identify false messages and evaluate the credibility of messages. In this paper, we take the messages and users on the mainstream Weibo as the research object, and combine the natural language processing, statistical learning and behavior analysis theory to construct the model. The research work of this paper is mainly reflected in the following two aspects: first, Weibo water army recognition research. Weibo users play a leading role in the Weibo platform, and recognizing Weibo Navy Army is an important process to evaluate the credibility of news. At present, the recognition of Weibo Navy Army is mostly human identification, so it is difficult to achieve the ideal effect in Weibo. By analyzing the differences in attributes and behaviors between ordinary users and navy troops in Weibo, this paper defines and identifies the effective features of the naval forces, makes an experimental comparison of the accuracy of the features, and makes use of the idea of probability map model. A WGM model for automatic identification of Weibo navy army is constructed. Taking the data from the two most representative Weibo platforms at home and abroad, Sina Weibo and Twitter, as experimental samples, a large number of comparative experiments have been carried out. The results show that the proposed features can be used to effectively identify the watermen in the Weibo platform. The WGM model is more accurate and effective than the traditional machine learning method. Second, Weibo rumour detection and information credibility research. It is a very important but difficult problem to assess the credibility of Weibo messages and to judge the authenticity of messages. Based on Weibo's comments, this paper defines three characteristics of support, content relevance and confidence to measure the differences between rumors and true Weibo, and constructs a BPCM model to evaluate the credibility of messages and judge whether the messages are true or false. Using the real data of Sina Weibo to compare and analyze the features and models in detail, the results show that the use of Weibo comments can effectively evaluate the credibility of the message and judge whether the message is true or false. The BPCM model can effectively solve the problem of message credibility evaluation and rumor detection. The research content of this topic is helpful to national public opinion monitoring, enterprise marketing analysis, field event analysis and accurate search.
【學(xué)位授予單位】：北京工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092

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