本文選題：社交網(wǎng)絡(luò) + 時(shí)間序列分析��； 參考：《北京郵電大學(xué)》2017年碩士論文

【摘要】：隨著信息社會(huì)的高速發(fā)展,信息傳播越來越趨向于多維化,互聯(lián)網(wǎng)作為飛速發(fā)展的新媒體,在信息傳播方面功不可沒。社交網(wǎng)絡(luò)的出現(xiàn)改變了人們獲取信息的方式,使得消息交換和共享變得快捷方便。社交網(wǎng)絡(luò)不斷涌現(xiàn)的熱門詞語,不僅影響著人們現(xiàn)實(shí)生活的觀點(diǎn)和立場(chǎng),而且很大程度上反映了社會(huì)輿論。因此,分析熱點(diǎn)內(nèi)容的時(shí)間序列傳播趨勢(shì),有效抑制負(fù)面消息傳播,促進(jìn)正面積極消息傳播,揭示其傳播動(dòng)力學(xué)特征,從而能進(jìn)一步分析輿論導(dǎo)向,具有非常重要的現(xiàn)實(shí)意義�；谏缃痪W(wǎng)絡(luò)熱點(diǎn)內(nèi)容的多元性和隨機(jī)性,本文從時(shí)間序列的角度來分析熱點(diǎn)內(nèi)容的傳播規(guī)律,同時(shí)引入模糊數(shù)學(xué)理論,提出模糊集分區(qū)算法進(jìn)行時(shí)間序列集合劃分,提出模糊趨勢(shì)分析算法來求解傳播趨勢(shì),反映了熱點(diǎn)內(nèi)容的傳播過程和突變規(guī)律。針對(duì)社交網(wǎng)絡(luò)熱點(diǎn)內(nèi)容傳播的場(chǎng)景,本文提出了一種基于模糊數(shù)學(xué)的熱點(diǎn)內(nèi)容時(shí)間序列預(yù)測(cè)模型。針對(duì)該模型,提出了模糊集分區(qū)算法,通過模糊隸屬函數(shù)對(duì)熱點(diǎn)內(nèi)容時(shí)間序列進(jìn)行模糊區(qū)間劃分,采用軟計(jì)算方法對(duì)熱詞傳播數(shù)值進(jìn)行靈活分區(qū),同時(shí)將異常值加以考慮。然后提出了模糊趨勢(shì)分析算法,分析單位時(shí)間數(shù)據(jù)對(duì)應(yīng)的模糊集合和傳播趨勢(shì),得出合理的預(yù)測(cè)結(jié)果。與傳統(tǒng)時(shí)間序列預(yù)測(cè)模型相比,本文提出的模糊時(shí)間序列分析預(yù)測(cè)模型不僅拓寬了時(shí)間序列分析在社交網(wǎng)絡(luò)的應(yīng)用范圍,同時(shí)更加準(zhǔn)確地分析社交網(wǎng)絡(luò)熱點(diǎn)內(nèi)容的傳播規(guī)律。在實(shí)驗(yàn)仿真驗(yàn)證方面,本文提出了基于模糊數(shù)學(xué)的時(shí)間序列分析預(yù)測(cè)模型,通過擬合程度和預(yù)測(cè)準(zhǔn)確度證明了算法的有效性。本文選取新浪微博熱點(diǎn)詞語作為實(shí)驗(yàn)數(shù)據(jù),采用海量熱詞數(shù)據(jù)對(duì)模糊時(shí)間序列預(yù)測(cè)模型進(jìn)行仿真。與傳統(tǒng)時(shí)間序列預(yù)測(cè)模型相比,模糊時(shí)間序列預(yù)測(cè)模型能夠更好地?cái)M合社交網(wǎng)絡(luò)熱詞的傳播過程,具有更高的預(yù)測(cè)準(zhǔn)確度。
[Abstract]:With the rapid development of the information society, the information communication tends to be multidimensional. The Internet, as a new media with rapid development, has contributed to the information dissemination.The emergence of social networks has changed the way people access information, making message exchange and sharing become fast and convenient.The popular words of social network not only affect people's viewpoint and stand in real life, but also reflect public opinion to a great extent.Therefore, it is of great practical significance to analyze the trend of time series communication of hot topics, effectively restrain the spread of negative news, promote the spread of positive news, reveal the dynamic characteristics of communication, and further analyze the orientation of public opinion.Based on the diversity and randomness of hot spots in social networks, this paper analyzes the propagation rules of hot spots from the point of view of time series, and introduces the theory of fuzzy mathematics, and proposes a fuzzy set partition algorithm for time series partitioning.A fuzzy trend analysis algorithm is proposed to solve the propagation trend, which reflects the propagation process and mutation law of hot topics.Aiming at the scene of hot content propagation in social networks, this paper presents a prediction model of hot content time series based on fuzzy mathematics.According to this model, a fuzzy set partition algorithm is proposed. The fuzzy interval partition of the time series of hot spots is carried out by fuzzy membership function, and the numerical value of hot word propagation is flexibly partitioned by soft computing method, and the outliers are considered at the same time.Then a fuzzy trend analysis algorithm is proposed to analyze the fuzzy set and propagation trend corresponding to the unit time data, and the reasonable prediction results are obtained.Compared with the traditional time series prediction model, the proposed fuzzy time series analysis and prediction model not only widens the scope of application of time series analysis in social networks, but also more accurately analyzes the propagation rules of hot spots in social networks.In the aspect of experimental simulation, a time series analysis and prediction model based on fuzzy mathematics is proposed. The validity of the algorithm is proved by the degree of fitting and the accuracy of prediction.In this paper, the hot words of Sina Weibo are selected as experimental data, and the fuzzy time series prediction model is simulated by massive hot word data.Compared with the traditional time series prediction model, the fuzzy time series prediction model can better fit the propagation process of social network hot words, and has higher prediction accuracy.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O159;O211.61

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