本文選題：微博 + 突發(fā)事件��； 參考：《武漢大學(xué)》2017年碩士論文

【摘要】：微博憑借其龐大的用戶群體,成功參與引爆一系列社會事件,比如地溝油事件,西南大旱事件等,這些事件均通過微博進入公眾視線,并在微博推動下向前推進。我國正處在突發(fā)公共事件的高發(fā)期,而且在未來一段時間內(nèi),我國都將面臨突發(fā)公共事件所帶來的嚴峻考驗。因此,通過微博數(shù)據(jù)探究城市突發(fā)事件的擴散特征,可以為政府應(yīng)對突發(fā)事件提供參考建議,及時向社會發(fā)布信息,引導(dǎo)輿論走向。文章基于微博開放API,獲取城市突發(fā)事件的數(shù)據(jù),經(jīng)過一系列數(shù)據(jù)預(yù)處理,將微博的文本信息,時間信息和地理信息進行擴散特征研究。利用ICTCLAS漢語分詞系統(tǒng),對微博的文本信息進行分詞,通過詞頻分析法探究事件發(fā)生的完整過程。對時間信息進行每天每小時數(shù)據(jù)統(tǒng)計,研究用戶使用微博的行為特征。利用重尾理論,分析微博的地理信息,發(fā)現(xiàn)突發(fā)事件發(fā)生后的地理擴散規(guī)律。重尾理論是一種病態(tài)的概率分布模型,盡管應(yīng)用領(lǐng)域廣泛,國內(nèi)還沒有把它應(yīng)用到地理空間分析。然后與熱點分析的結(jié)果進行比較,發(fā)現(xiàn)可行性。通過回歸分析確定信息擴散與事件發(fā)生地點是否有關(guān)聯(lián)。文章以"杭州公交燃燒案"為例,進行城市突發(fā)事件的擴散特征研究。將文本中排名前十的名詞、動詞、形容詞進行排序,可以清楚地看出事件的走向,從懷疑是人為事件到嫌疑人身份的確認,只用了四天時間。微博用戶的活動事件具有一定的規(guī)律性,總體上呈現(xiàn)冪律分布,每天有固定的活躍時間段。對比熱點分析的結(jié)果,重尾理論可用于地理空間分析。在突發(fā)事件發(fā)生地點的周圍,公眾的關(guān)注程度受距離的影響,但大城市對事件的關(guān)注不太可能受到距離的影響。綜上,充分探究城市突發(fā)事件發(fā)生后的微博數(shù)據(jù),從新的角度分析其位置信息,是順應(yīng)快速發(fā)展和政務(wù)微博的需要。
[Abstract]:With its huge user base, Weibo has been involved in detonating a series of social events, such as the gutter oil incident, the southwest drought and so on, all of which have been brought into the public eye through Weibo and pushed forward by Weibo. China is in a period of high incidence of public emergencies, and in the future, our country will be faced with the severe test brought by public emergencies. Therefore, exploring the diffusion characteristics of urban emergencies through Weibo data can provide reference suggestions for the government to deal with emergencies, release information to the society in time, and guide public opinion. In this paper, the data of urban emergencies are obtained based on Weibo. After a series of data preprocessing, the diffusion characteristics of text information, time information and geographic information of Weibo are studied. Using the ICTCLAS Chinese word segmentation system, the text information of Weibo is partitioned, and the complete process of the event is explored by word frequency analysis. The daily hourly data of time information are analyzed to study the behavior characteristics of users using Weibo. Based on the theory of heavy tail, the geographic information of Weibo is analyzed, and the law of geographic diffusion after unexpected events is found. Heavy-tailed theory is a ill-conditioned probability distribution model. Although it is widely used in many fields, it has not been applied to geospatial analysis in China. Then compared with the results of hot spot analysis, the feasibility was found. Regression analysis is used to determine whether the information diffusion is related to the location of the event. Taking the case of Hangzhou bus Burning as an example, this paper studies the diffusion characteristics of urban emergencies. By sorting the top ten nouns, verbs and adjectives in the text, we can clearly see the trend of the events, from suspicion of human events to confirmation of suspects' identity, in only four days. The Weibo user's active events have certain regularity, which is power law distribution in general, and has a fixed active time period every day. Compared with the results of hot spot analysis, the heavy-tailed theory can be used in geospatial analysis. The attention of the public is affected by distance, but the attention of big cities is not likely to be affected by distance. In summary, it is necessary to fully explore the Weibo data of urban emergencies and analyze the location information from a new perspective. It is necessary to adapt to the rapid development and the need of government Weibo.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：D63

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1 張超越;基于微博的城市突發(fā)事件擴散特征研究[D];武漢大學(xué);2017年

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本文編號：1882825