【摘要】：微博作為新興的社交網(wǎng)絡(luò)媒體,以其傳播快、時(shí)效性強(qiáng)、內(nèi)容全面的優(yōu)勢(shì)成為突發(fā)事件信息快速聚集和傳播的重要渠道。但指數(shù)增長的微博數(shù)據(jù)使得用戶難以及時(shí)了解整個(gè)事件的細(xì)節(jié)信息,且微博自由化程度高,突發(fā)事件在微博上容易被惡意傳播,給國家安全和社會(huì)穩(wěn)定帶來了極大的隱患。因此從海量微博中準(zhǔn)確而高效地檢測(cè)出突發(fā)事件具有重要的意義,不僅可以幫助用戶實(shí)時(shí)獲取重要的突發(fā)事件資訊,消除突發(fā)事件帶來的恐慌心理,還能夠協(xié)助應(yīng)急管理機(jī)構(gòu)實(shí)時(shí)把握突發(fā)事件的發(fā)展態(tài)勢(shì),合理地控制和引導(dǎo)輿論發(fā)展方向,為輿情應(yīng)急管理提供決策信息支持。微博因噪聲大、文本短小稀疏、不規(guī)范等特點(diǎn)給突發(fā)事件檢測(cè)帶來了挑戰(zhàn),本文通過分析突發(fā)事件發(fā)生時(shí)期的爆發(fā)特性,結(jié)合微博數(shù)據(jù)的特點(diǎn),對(duì)以突發(fā)特征為中心的突發(fā)事件檢測(cè)方法及其輿情熱度分析進(jìn)行了深入研究。突發(fā)事件檢測(cè)上,首先在綜合考慮詞語的主題表達(dá)能力和突發(fā)性的基礎(chǔ)上,引入?yún)⒄諘r(shí)間窗機(jī)制,設(shè)計(jì)了詞頻、文檔頻率、話題標(biāo)簽Hashtag、詞頻增長率四類特征選擇與計(jì)算方法,提出了基于動(dòng)態(tài)閾值的突發(fā)主題詞抽取算法,實(shí)驗(yàn)結(jié)果表明該方法可以準(zhǔn)確的提取有效表征事件的突發(fā)主題詞。然后提出了基于突發(fā)主題詞和凝聚式層次聚類的突發(fā)事件檢測(cè)算法。該算法以突發(fā)主題詞作為突發(fā)特征,將微博文本表示為特征向量,引入微博事件三要素過濾策略保留高質(zhì)量的微博,以Jaccard計(jì)算重合度作為相似度衡量標(biāo)準(zhǔn)構(gòu)造微博文本相似度矩陣,使用凝聚式層次聚類算法實(shí)現(xiàn)了突發(fā)事件的檢測(cè)。實(shí)驗(yàn)結(jié)果表明,突發(fā)事件檢測(cè)方法達(dá)到了80%的準(zhǔn)確率,驗(yàn)證了該方法的可行性和有效性。針對(duì)檢測(cè)的突發(fā)事件,對(duì)微博用戶網(wǎng)絡(luò)特征和微博傳播方式分析,從用戶影響力和微博傳播影響力兩個(gè)視角提出了突發(fā)事件的輿情熱度計(jì)算模型,并構(gòu)造單位時(shí)間片進(jìn)行輿情熱度的時(shí)序變化分析,通過實(shí)例分析發(fā)現(xiàn),該模型能夠較準(zhǔn)確的劃分突發(fā)事件的輿情生命周期,從整體上了解突發(fā)事件的發(fā)展趨勢(shì)及變化規(guī)律。
[Abstract]:Weibo, as a new social network media, has become an important channel for the rapid gathering and dissemination of emergency information with its advantages of fast dissemination, strong timeliness and comprehensive content. However, the exponential growth of Weibo data makes it difficult for users to know the details of the whole incident in time, and Weibo has a high degree of liberalization, and sudden events are easily spread maliciously on Weibo, which brings great hidden trouble to national security and social stability. Therefore, it is of great significance to detect emergencies accurately and efficiently from the mass of Weibo. It can not only help users to obtain important emergency information in real time, but also eliminate panic caused by emergencies. It can also help emergency management organizations to grasp the development of emergencies in real time, reasonably control and guide the direction of public opinion development, and provide decision-making information support for public opinion emergency management. Weibo has brought challenges to the detection of emergencies because of the characteristics of high noise, short and sparse text, non-standard and so on. This paper analyzes the burst characteristics of the burst period and combines the characteristics of Weibo data. In this paper, the method of emergency detection and the thermal analysis of public opinion based on the burst feature are studied in depth. In emergency detection, on the basis of comprehensive consideration of the topic expression ability and the outburst of words, the reference time window mechanism is introduced, and four kinds of feature selection and calculation methods are designed, such as word frequency, document frequency and topic label Hashtag, word frequency growth rate. A burst topic word extraction algorithm based on dynamic threshold is proposed. The experimental results show that this method can accurately extract the burst topic words which represent the event effectively. Then, a burst detection algorithm based on burst topic words and condensed hierarchical clustering is proposed. In this algorithm, burst topic words are used as burst features, Weibo text is represented as feature vector, and Weibo event three-factor filtering strategy is introduced to retain high-quality Weibo. The similarity matrix of Weibo text is constructed by using Jaccard computing coincidence degree as the similarity measure, and the detection of unexpected events is realized by using the condensed hierarchical clustering algorithm. The experimental results show that the accuracy of the method is 80%, which verifies the feasibility and effectiveness of the method. Aiming at the detected emergencies, this paper analyzes the characteristics of Weibo's user network and Weibo's mode of communication, and puts forward a public opinion heat calculation model from the perspectives of user's influence and Weibo's communication influence. A unit time slice is constructed to analyze the time series change of public opinion heat. It is found that the model can accurately divide the life cycle of public opinion of sudden events and understand the development trend and changing rule of sudden events as a whole.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.092;G206
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本文編號(hào)：2439892