本文選題：突發(fā)熱點(diǎn)事件 + 可信度分析�。� 參考：《哈爾濱工業(yè)大學(xué)》2013年碩士論文

【摘要】：近年來，社會化媒體獲得高速發(fā)展，人們的意見表達(dá)空間得到了空前擴(kuò)展。以微博為代表的社會化媒體正在成為很多熱點(diǎn)事件的首發(fā)平臺，如何快速發(fā)現(xiàn)和檢測社會化媒體中的突發(fā)熱點(diǎn)事件，對輿情分析等應(yīng)用來說至關(guān)重要。同時(shí)在社會化媒體中，捕風(fēng)捉影、造謠生事的情況時(shí)常發(fā)生，造成惡劣影響。對社會化媒體中的事件進(jìn)行可信度評估并識別網(wǎng)絡(luò)謠言，可以降低其不良影響，維護(hù)經(jīng)濟(jì)和社會穩(wěn)定。 目前，突發(fā)熱點(diǎn)事件的檢測主要通過檢測熱詞來發(fā)現(xiàn)事件，在實(shí)際應(yīng)用中往往存在著將周期性突發(fā)事件和短時(shí)間內(nèi)集中發(fā)布的廣告誤識為突發(fā)熱點(diǎn)的問題；在社會化媒體事件可信度分析研究中，目前主要的分析方法有基于可信度排序和基于分類器判別兩種思路，但大部分方法未考慮用戶的觀點(diǎn)和情感傾向性對謠言事件判別的作用。此外，對用戶特征的挖掘也存在不足之處。 針對以上問題，本文對突發(fā)熱點(diǎn)事件的檢測及其可信度分析方法進(jìn)行了研究。首先，本文設(shè)計(jì)實(shí)現(xiàn)了一種基于熱詞識別和原創(chuàng)度過濾的突發(fā)熱點(diǎn)事件檢測方法。首先利用微博的文本內(nèi)容及其傳播特性，挖掘出突發(fā)熱詞。然后對熱詞進(jìn)行聚類，形成高度相關(guān)的簇，從而發(fā)現(xiàn)突發(fā)熱點(diǎn)事件。此外，本文提出利用話題原創(chuàng)度為主要特征，對在內(nèi)容和傳播規(guī)律上酷似熱點(diǎn)的廣告類事件進(jìn)行過濾的方法，有效提高了突發(fā)熱點(diǎn)事件檢測的精度。在此基礎(chǔ)上，本文研究了基于特征挖掘的事件可信度分析和謠言檢測方法。針對檢測到的突發(fā)熱點(diǎn)事件，，通過利用事件在文本內(nèi)容、發(fā)表用戶特征、話題以及在社會化媒體中的傳播特性等特征，構(gòu)造分類器發(fā)現(xiàn)虛假謠言事件。 本文的主要貢獻(xiàn)包括：第一，本文設(shè)計(jì)實(shí)現(xiàn)了一種利用回顧窗口，綜合考慮詞語的詞頻及其增長速度進(jìn)行熱詞識別的方法，有效改善了周期性事件誤檢的問題；第二，本文提出和設(shè)計(jì)話題原創(chuàng)度指標(biāo)，并用于對應(yīng)用環(huán)境中常見的廣告事件進(jìn)行過濾，提高了突發(fā)熱點(diǎn)事件檢測準(zhǔn)確率；最后，本文提出的利用多視角特征進(jìn)行事件可信度分析的方法，可以較好地檢測社會化媒體中的謠言。文中提出的一系列謠言事件判別特征對相關(guān)領(lǐng)域的研究也有很好的促進(jìn)作用。
[Abstract]:In recent years, with the rapid development of social media, people's opinion expression space has been expanded unprecedented. Social media, represented by Weibo, is becoming the starting platform for many hot events. How to quickly detect and detect unexpected hot events in social media is very important to the application of public opinion analysis. At the same time, in the social media, speculation, rumour-making things often occur, causing adverse effects. Evaluating the credibility of events in social media and identifying online rumors can reduce its adverse effects and maintain economic and social stability. At present, the detection of hot spots is mainly through the detection of hot words to find events, in practical applications, there is often a periodic emergency and a short period of time to focus on the issue of advertising issued as hot spots; In the research of reliability analysis of social media events, the main analysis methods are based on credibility ranking and classifier discrimination. However, most of the methods do not consider the role of user's viewpoint and emotional tendency in judging rumor events. In addition, the mining of user features also has shortcomings. In order to solve the above problems, this paper studies the detection and reliability analysis of hot spots. Firstly, this paper designs and implements a hot spot detection method based on hot word recognition and originality filtering. First of all, by using Weibo's text content and its spreading characteristics, the burst hot words are excavated. Then the hot words are clustered to form a highly relevant cluster, and then the sudden hot events are discovered. In addition, this paper proposes a method of filtering advertising events which closely resemble hot spots in content and propagation law by using topic originality as the main feature, which can effectively improve the accuracy of the detection of sudden hot spots. On this basis, this paper studies the event reliability analysis and rumor detection method based on feature mining. Based on the features of events in text, user features, topics and propagation in social media, a classifier is constructed to detect false rumors. The main contributions of this paper are as follows: first, this paper designs and implements a method to identify hot words by taking into account the word frequency and growth rate of words, which can effectively improve the problem of periodic event misdetection. This paper puts forward and designs the index of topic originality, and it is used to filter the common advertisement events in the application environment, which improves the detection accuracy of sudden hot events. In this paper, the method of event reliability analysis based on multi-view features can detect rumors in social media. A series of discriminant features of rumour events proposed in this paper also contribute to the research of related fields.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 鄭斐然;苗奪謙;張志飛;高燦;;一種中文微博新聞話題檢測的方法[J];計(jì)算機(jī)科學(xué);2012年01期

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