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Big Data Challenges and Opportunities: Information Diffusion, User Behavior, and Informational Trends in Online Social Networks.

機譯：大數(shù)據(jù)挑戰(zhàn)和機遇：在線社交網(wǎng)絡中的信息傳播，用戶行為和信息趨勢。

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Social networks have permeated our daily lives. We transmit ideas, innovations, news, and even diseases through them. They affect the products we buy, the languages we speak and the behaviors we exhibit. Given such implications, an accurate understanding of social networks is crucial. In addition, with most social interactions moving online, researchers have access to unprecedented amounts of detailed data about social interactions. Therefore, we are at a point in history in which both the motivation and the opportunity to study social networks is overwhelmingly strong, attracting researchers from various backgrounds to social networks research. Naturally researchers, whether they are from databases, machine learning or theory background, have a tendency to apply techniques from their fields directly to this new paradigm. However, given the interdisciplinary nature of problems in social networks, one view point is insufficient in capturing the essence of these problems. The main goal of this dissertation is to bring knowledge from various backgrounds to tackle problems relating to social networks research. In addition to relying on diverse research fields, we also leverage the power of big data. The unprecedented amounts of data on online human interactions present great opportunities for the study of social networks. As demonstrated in this thesis, big data can help build better models, algorithms and infrastructures in social networks research.;The entirety of the vast space of problems relating to social networks research is likely too complex to summarize in one thesis. Instead, we focus on problems relating to information diffusion in online social networks. The overreaching goal of this thesis is to develop useful tools for understanding, managing and reporting on information diffusion by leveraging various research areas such as data mining, statistics, data management, theory and social sciences, rather than relying on only one. While identifying influentials in social networks, we leverage data-driven methods. When modeling diffusion of information and user behavior, we rely on statistical methods and theories from social science literature. Given a solid understanding of information diffusion in social networks, we can focus on various applications. Discrete math optimization techniques provide us an optimal direction to limiting the spread of misinformation in social networks. And finally, we rely on data streams solutions for building an informational trend detection framework in social networks. Throughout our studies, we focus on various networks such as Twitter, Digg, Facebook and the Blogosphere.
機譯：社交網(wǎng)絡已經(jīng)滲透到我們的日常生活中。我們通過它們傳播思想，創(chuàng)新，新聞，甚至疾病。它們會影響我們購買的產(chǎn)品，我們的語言和我們表現(xiàn)出的行為。鑒于這樣的含義，對社交網(wǎng)絡的準確理解至關(guān)重要。此外，隨著大多數(shù)社交互動在線上轉(zhuǎn)移，研究人員可以訪問前所未有的有關(guān)社交互動的詳細數(shù)據(jù)。因此，我們正處于歷史上的一個時刻，研究社交網(wǎng)絡的動機和機會都非常強大，吸引了來自不同背景的研究人員從事社交網(wǎng)絡研究。自然地，無論是來自數(shù)據(jù)庫，機器學習還是理論背景的研究人員，都有將自己領(lǐng)域的技術(shù)直接應用于這種新范式的趨勢。但是，鑒于社會網(wǎng)絡中問題的跨學科性質(zhì)，一種觀點不足以捕捉這些問題的實質(zhì)。本文的主要目的是從各種背景中獲取知識，以解決與社會網(wǎng)絡研究有關(guān)的問題。除了依賴不同的研究領(lǐng)域，我們還利用大數(shù)據(jù)的力量。在線人際互動的前所未有的數(shù)據(jù)量為社交網(wǎng)絡的研究提供了巨大的機會。正如本文所論證的那樣，大數(shù)據(jù)可以幫助在社交網(wǎng)絡研究中建立更好的模型，算法和基礎(chǔ)結(jié)構(gòu)。與社交網(wǎng)絡研究有關(guān)的問題的巨大空間的整體可能太復雜而無法在一個論文中進行總結(jié)。相反，我們專注于與在線社交網(wǎng)絡中的信息傳播有關(guān)的問題。本文的首要目標是通過利用數(shù)據(jù)挖掘，統(tǒng)計，數(shù)據(jù)管理，理論和社會科學等各個研究領(lǐng)域，而不是僅依靠一個領(lǐng)域，來開發(fā)有用的工具來理解，管理和報告信息傳播。在確定社交網(wǎng)絡中的影響力時，我們利用數(shù)據(jù)驅(qū)動的方法。在對信息和用戶行為的擴散進行建模時，我們依賴于社會科學文獻中的統(tǒng)計方法和理論。有了對社交網(wǎng)絡中信息傳播的深入了解，我們可以專注于各種應用程序。離散數(shù)學優(yōu)化技術(shù)為我們提供了一種限制社交網(wǎng)絡中錯誤信息傳播的最佳方向。最后，我們依靠數(shù)據(jù)流解決方案在社交網(wǎng)絡中構(gòu)建信息趨勢檢測框架。在整個研究過程中，我們專注于Twitter，Digg，F(xiàn)acebook和Blogosphere等各種網(wǎng)絡。

著錄項

  • 作者

    Budak, Ceren.;

  • 作者單位

    University of California, Santa Barbara.;

  • 授予單位 University of California, Santa Barbara.;
  • 學科 Web Studies.;Computer Science.
  • 學位 Ph.D.
  • 年度 2012
  • 頁碼 257 p.
  • 總頁數(shù) 257
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

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