Social media analytics: a survey of techniques, tools and platforms

被引:310
作者
Batrinca, Bogdan [1 ]
Treleaven, Philip C. [1 ]
机构
[1] UCL, Dept Comp Sci, Gower St, London WC1E 6BT, England
基金
英国工程与自然科学研究理事会;
关键词
Social media; Scraping; Behavior economics; Sentiment analysis; Opinion mining; NLP; Toolkits; Software platforms;
D O I
10.1007/s00146-014-0549-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an 'explosion' of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing.
引用
收藏
页码:89 / 116
页数:28
相关论文
共 30 条
[1]
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
[2]
BOLLEN J, 2011, J COMPUT SCI-NETH, V2, P1, DOI DOI 10.1016/j.jocs.2010.12.007
[3]
SECRET: A Model for Analysis of the Execution Semantics of Stream Processing Systems [J].
Botan, Irina ;
Derakhshan, Roozbeh ;
Dindar, Nihal ;
Haas, Laura ;
Miller, Renee J. ;
Tatbul, Nesime .
PROCEEDINGS OF THE VLDB ENDOWMENT, 2010, 3 (01) :232-243
[4]
DATA STREAM MANAGEMENT SYSTEMS FOR COMPUTATIONAL FINANCE [J].
Chandramouli, Badrish ;
Ali, Mohamed ;
Goldstein, Jonathan ;
Sezgin, Beysim ;
Raman, Balan Sethu .
COMPUTER, 2010, 43 (12) :45-52
[5]
Chandrasekar C, 2011, INT J COMPUT APPL, V1, P6
[6]
Computational social science [J].
Cioffi-Revilla, Claudio .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (03) :259-271
[7]
Galas M., 2012, P COMPUTATIONAL SOCI, V1, P1
[8]
Hebrail G., 2008, MINING MASSIVE DATA, P89
[9]
Hirudkar A., 2013, INT J COMPUTING SCI, V6, P232
[10]
If you love something, let it go mobile: Mobile marketing and mobile social media 4x4 [J].
Kaplan, Andreas M. .
BUSINESS HORIZONS, 2012, 55 (02) :129-139