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題名 巨量資料生態：以模因學分析網路文本的進化
The Ecology of Big Data: A Memetic Approach on the Evolution of Online Text作者 柯政豪
Ke, Cheng Hao貢獻者 蕭乃沂
Hsiao, Naiyi
柯政豪
Ke, Cheng Hao關鍵詞 文字探勘
巨量資料
模因學
進化論
生態學
Text Mining
Big Data
Memetics
Darwinian Evolution
Ecology日期 2016 上傳時間 11-七月-2016 17:43:20 (UTC+8) 摘要 公共行政學正面臨理論與方法無法結合的挑戰，這個問題在巨量資料的時代更是明顯，因為與人脫離的網路文本資料無法透過傳統的理論架構來解釋與分析，如果不引進新的本體論與認識論，公共行政學將遭遇無法深入分析巨量文本資料的困境。近代達爾文進化論因為透過複製者角度解釋自然界現象，所以能夠脫離以個別生物為核心的觀點，解釋過去無法分析的行為與動機。以進化論為基礎的模因學將社會文化的變化視為一種進化的過程，認為社會文化界的變化可以透過模因這種複製單位的進化來解釋，模因的觀點因為脫離以人為核心的角度，所以能夠解釋無法和使用者結合的網路文本資料。 本研究首先透過整合過去社會文化進化和模因學的研究，建構脫離使用者的巨量文本資料分析架構與假設，同時引入進化論與生態學中的方法與理論，驗證巨量網路文本背後的進化動態與機制。接著本研究針對Yahoo!奇摩新聞中探討「課綱微調」議題的1,761篇主文進行分析，透過中文斷詞與文本集群分析方法萃取出由不同模因類種建構的主文集群，並透過量化統計方法驗證個別集群文本特質與在時間上分布的趨勢對於整體主文集群變化的影響。結果發現個別模因類種本身在時間上的分布擁有密度依賴的關係，同時主文本身的情緒、字數等等特質，對產生的回文數也都有影響，但這些影響在不同密度的環境下會有所變化，網站版面環境本身也會與主文回文數有顯著的關係，而個別模因類種之間互動也都有顯著的正向交叉關係，另外模因類種也與詞彙組之間有顯著的正向和負向關係。 本研究的結果顯示以模因學探討巨量文本資料能夠允許研究者結合理論與文本探勘技術，以理論為出發點建構出能夠被驗證的假設，並引入進化論和生態學中的方法與架構進行資料的分析。研究發現由不同詞彙建構的文本數量在時間上的分布是一件必須觀察的現象，因為由類同詞彙建構的文本在過去時間點的變化，對於當下時間點類同主文的數量會有影響。同時文本特質和所處的網路環境也都是觀測網路文本變化時不可忽略的變數，因為這些因素對主文和回文的數量都有很重要的影響。另外分析網路文本時，應該以網站來區隔分析資料，因為網路環境同樣對於文本與回文的數量有影響。最後，出現在同一篇主文中機率高的詞彙組合也是會影響文本變化的重要因素之一。 本研究建議未來巨量文本資料的研究應該繼續以模因學的觀點出發，同時搭配具有不同優缺點的文本探勘技術與密度依賴檢定反覆驗證結果。未來的研究也宜朝向分析時間序列更長和出自不同網站的文本集，並建構能夠整合不同文本特質和環境影響的分析架構，另外納入文字以外的多媒體資料和以模因學角度設計的問卷調查資料進行探討。
The mismatch between theory and method is a crisis which the discipline of public administration cannot afford to ignore. The arrival of the “Era of Big Data”, only serves to make matters worse. As data becomes uncoupled with the individual, so goes any pretense of trying to provide analyses beyond that of mere description. If public administration refuses to import new ontology and epistemology, then very little could be gained from online text research. The Darwinian theory of evolution, ever since the Modern Synthesis, has embraced the replicator centered point of view when explaining all living phenomena. This has unshackled the theory from limitations of the traditional individual centered view of evolution. Memetics is a recent offshoot of the theory of evolution. It views social cultural change as a process based on the evolution of a cultural unit of selection, the meme. Due to memetics’ ability to explain social cultural evolution from the meme’s point of view, it is a natural candidate to examine the dynamics of “big” online text data. The first part of this research is on the construction of an online text analysis framework, with testable hypotheses, through the integration of past literature on evolution, social cultural evolution, memetics and ecology. The second part is concerned with the testing of the framework with empirical data. The text corpus used in this research contains 1,761 news reports from the Yahoo! News website on the issue of high school curriculum change. Chinese term segmentation and text clustering algorithms were applied to the corpus, in order to extract text quasi-species composed of similar memes. Statistical tests were then used to determine the influence of text characteristics and temporal distribution dynamics on the population of quasi-species. Findings indicate that the population dynamics of text quasi-species were influenced by density dependence. Text characteristics, such as word length and sentiment, also exert significant influence on the number of comments that each text receives. However, these influences are not equal under different density conditions. The location of the news articles within the website also creates a difference in the number of comments received. Finally, interactions between the temporal distribution of different quasi-species and between quasi-species and term groups also yielded significant positive and negative correlations. The results are proof that memetics is an ideal theoretical platform to connect theory with text mining/analysis methods. It allows for a theory based approach and the creation of testable hypotheses. Frameworks and methods based on evolution and ecological research are also applicable under memetics. The empirical findings point to the importance of monitoring the temporal distribution of online text, and the significance of text characteristics and website environments to text population changes. The results also illustrate the importance of term groups in the influence of text population dynamics. Together these variables and effects are all central to the understanding of the change in online text and comment numbers, and the effect of past text population on current population changes. Online texts from different websites should also be analyzed separately. This research recommends that future public administration big data analyses should continue to adopt the memetic approach. Nevertheless, attention should be given to the strengths and weaknesses of different text mining algorithms and density dependence tests. Big data time series from different websites and with longer temporal spans should also be considered, while social cultural artifacts other than texts should not be excluded from memetics based researches. New frameworks must also be constructed to integrate and understand, the interaction between important variables, such as, text characteristics and environmental influences. 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國立政治大學
公共行政學系
102256006資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102256006 資料類型 thesis dc.contributor.advisor 蕭乃沂 zh_TW dc.contributor.advisor Hsiao, Naiyi en_US dc.contributor.author (作者) 柯政豪 zh_TW dc.contributor.author (作者) Ke, Cheng Hao en_US dc.creator (作者) 柯政豪 zh_TW dc.creator (作者) Ke, Cheng Hao en_US dc.date (日期) 2016 en_US dc.date.accessioned 11-七月-2016 17:43:20 (UTC+8) - dc.date.available 11-七月-2016 17:43:20 (UTC+8) - dc.date.issued (上傳時間) 11-七月-2016 17:43:20 (UTC+8) - dc.identifier (其他 識別碼) G0102256006 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98907 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 公共行政學系 zh_TW dc.description (描述) 102256006 zh_TW dc.description.abstract (摘要) 公共行政學正面臨理論與方法無法結合的挑戰，這個問題在巨量資料的時代更是明顯，因為與人脫離的網路文本資料無法透過傳統的理論架構來解釋與分析，如果不引進新的本體論與認識論，公共行政學將遭遇無法深入分析巨量文本資料的困境。近代達爾文進化論因為透過複製者角度解釋自然界現象，所以能夠脫離以個別生物為核心的觀點，解釋過去無法分析的行為與動機。以進化論為基礎的模因學將社會文化的變化視為一種進化的過程，認為社會文化界的變化可以透過模因這種複製單位的進化來解釋，模因的觀點因為脫離以人為核心的角度，所以能夠解釋無法和使用者結合的網路文本資料。 本研究首先透過整合過去社會文化進化和模因學的研究，建構脫離使用者的巨量文本資料分析架構與假設，同時引入進化論與生態學中的方法與理論，驗證巨量網路文本背後的進化動態與機制。接著本研究針對Yahoo!奇摩新聞中探討「課綱微調」議題的1,761篇主文進行分析，透過中文斷詞與文本集群分析方法萃取出由不同模因類種建構的主文集群，並透過量化統計方法驗證個別集群文本特質與在時間上分布的趨勢對於整體主文集群變化的影響。結果發現個別模因類種本身在時間上的分布擁有密度依賴的關係，同時主文本身的情緒、字數等等特質，對產生的回文數也都有影響，但這些影響在不同密度的環境下會有所變化，網站版面環境本身也會與主文回文數有顯著的關係，而個別模因類種之間互動也都有顯著的正向交叉關係，另外模因類種也與詞彙組之間有顯著的正向和負向關係。 本研究的結果顯示以模因學探討巨量文本資料能夠允許研究者結合理論與文本探勘技術，以理論為出發點建構出能夠被驗證的假設，並引入進化論和生態學中的方法與架構進行資料的分析。研究發現由不同詞彙建構的文本數量在時間上的分布是一件必須觀察的現象，因為由類同詞彙建構的文本在過去時間點的變化，對於當下時間點類同主文的數量會有影響。同時文本特質和所處的網路環境也都是觀測網路文本變化時不可忽略的變數，因為這些因素對主文和回文的數量都有很重要的影響。另外分析網路文本時，應該以網站來區隔分析資料，因為網路環境同樣對於文本與回文的數量有影響。最後，出現在同一篇主文中機率高的詞彙組合也是會影響文本變化的重要因素之一。 本研究建議未來巨量文本資料的研究應該繼續以模因學的觀點出發，同時搭配具有不同優缺點的文本探勘技術與密度依賴檢定反覆驗證結果。未來的研究也宜朝向分析時間序列更長和出自不同網站的文本集，並建構能夠整合不同文本特質和環境影響的分析架構，另外納入文字以外的多媒體資料和以模因學角度設計的問卷調查資料進行探討。 zh_TW dc.description.abstract (摘要) The mismatch between theory and method is a crisis which the discipline of public administration cannot afford to ignore. The arrival of the “Era of Big Data”, only serves to make matters worse. As data becomes uncoupled with the individual, so goes any pretense of trying to provide analyses beyond that of mere description. If public administration refuses to import new ontology and epistemology, then very little could be gained from online text research. The Darwinian theory of evolution, ever since the Modern Synthesis, has embraced the replicator centered point of view when explaining all living phenomena. This has unshackled the theory from limitations of the traditional individual centered view of evolution. Memetics is a recent offshoot of the theory of evolution. It views social cultural change as a process based on the evolution of a cultural unit of selection, the meme. Due to memetics’ ability to explain social cultural evolution from the meme’s point of view, it is a natural candidate to examine the dynamics of “big” online text data. The first part of this research is on the construction of an online text analysis framework, with testable hypotheses, through the integration of past literature on evolution, social cultural evolution, memetics and ecology. The second part is concerned with the testing of the framework with empirical data. The text corpus used in this research contains 1,761 news reports from the Yahoo! News website on the issue of high school curriculum change. Chinese term segmentation and text clustering algorithms were applied to the corpus, in order to extract text quasi-species composed of similar memes. Statistical tests were then used to determine the influence of text characteristics and temporal distribution dynamics on the population of quasi-species. Findings indicate that the population dynamics of text quasi-species were influenced by density dependence. Text characteristics, such as word length and sentiment, also exert significant influence on the number of comments that each text receives. However, these influences are not equal under different density conditions. The location of the news articles within the website also creates a difference in the number of comments received. Finally, interactions between the temporal distribution of different quasi-species and between quasi-species and term groups also yielded significant positive and negative correlations. The results are proof that memetics is an ideal theoretical platform to connect theory with text mining/analysis methods. It allows for a theory based approach and the creation of testable hypotheses. Frameworks and methods based on evolution and ecological research are also applicable under memetics. The empirical findings point to the importance of monitoring the temporal distribution of online text, and the significance of text characteristics and website environments to text population changes. The results also illustrate the importance of term groups in the influence of text population dynamics. Together these variables and effects are all central to the understanding of the change in online text and comment numbers, and the effect of past text population on current population changes. Online texts from different websites should also be analyzed separately. This research recommends that future public administration big data analyses should continue to adopt the memetic approach. Nevertheless, attention should be given to the strengths and weaknesses of different text mining algorithms and density dependence tests. Big data time series from different websites and with longer temporal spans should also be considered, while social cultural artifacts other than texts should not be excluded from memetics based researches. New frameworks must also be constructed to integrate and understand, the interaction between important variables, such as, text characteristics and environmental influences. Findings on all forms of online data would also be enhanced through comparisons with results from questionnaires designed with memetics in mind. en_US dc.description.tableofcontents 第一章 緒論 3第一節 進化論與模因的簡介 5第二節 公共行政為什麼需要引入進化的概念 7第三節 目前巨量資料與網路民意的限制 12第四節 研究動機與目的 17第二章 文獻回顧 21第一節 近代達爾文進化論發展與概念 21第二節 模因學的發展與概念 40第三節 為何透過模因分析文本？ 61第三章 研究設計與方法 65第一節 網路文本的模因定義與測量 65第二節 網路文本資料 74第三節 研究流程與假設 80第四節 網路文本分析與演算法 86第五節 資料分析方法 95第四章 研究分析與結果 99第一節 斷詞與文本集群分析 100第二節 密度依賴選擇的影響 115第三節 文本特質的影響 130第四節 生態互動的影響 138第五節 環境的影響 143第六節 遺傳漂變的影響 147第七節 詞彙組的影響 151第八節 研究發現 169第五章 結論 179第一節 對於巨量資料分析的貢獻 180第二節 實務建議 183第三節 研究限制與後續研究建議 184參考文獻 187中文文獻 187英文文獻 187附錄1 211 zh_TW dc.format.extent 5254659 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102256006 en_US dc.subject (關鍵詞) 文字探勘 zh_TW dc.subject (關鍵詞) 巨量資料 zh_TW dc.subject (關鍵詞) 模因學 zh_TW dc.subject (關鍵詞) 進化論 zh_TW dc.subject (關鍵詞) 生態學 zh_TW dc.subject (關鍵詞) Text Mining en_US dc.subject (關鍵詞) Big Data en_US dc.subject (關鍵詞) Memetics en_US dc.subject (關鍵詞) Darwinian Evolution en_US dc.subject (關鍵詞) Ecology en_US dc.title (題名) 巨量資料生態：以模因學分析網路文本的進化 zh_TW dc.title (題名) The Ecology of Big Data: A Memetic Approach on the Evolution of Online Text en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 李仲彬（2013a）。政府創新的類型與分佈：我國地方政府1999-2010年間的觀察。公共行政學報，44，73-112。李仲彬（2013b）。影響地方政府創新產出量的因素。東吳政治學報，31(1)，105-160。林千翔、張嘉惠、陳貞伶（2010）。結合長詞優先與序列標記之中文斷詞研究。中文計算語言學期刊(International Journal of Computational Linguistics and Chinese Language Processing)，15(3-4)，161-180。高婉瑜（2014）。網路語言的語音模因及其傳播—以宅女小紅的作品為例。淡江中文學報，30，277-315。陳良駒、張正宏、陳日鑫（2010）。以特徵詞共現特性探討知識管理研究議題相關性—使用共詞與關聯法則分析。資訊管理研究，17(4)，31-60。陳稼興、謝佳倫、許芳誠（2000）。以遺傳演算法為基礎的中文斷詞研究。資訊管理研究，2(2)，27-44。廖洲棚、陳敦源、蕭乃沂、廖興中（2014）。運用巨量資料實踐良善治理：網路民意導入政府決策分析之可行性研究。行政院研究發展考核委員會（編號：RDEC-MIS-102-003），未出版。蕭乃沂、陳敦源、廖洲棚（2015）。政府應用巨量資料精進公共服務與政策分析之可行性研究。國家發展委員會（編號：NDC-MIS-103-003），未出版。蕭乃沂、黃東益（2015）。104年度網路輿情蒐集、分析及運用計畫結案報告。教育部青年發展署，未出版。Adar, E., & Adamic, L. 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