1
http://www.emnlp2015.org/tutorials/3/3_OptionalAttachment.pdf
https://www.cs.cmu.edu/~ark/EMNLP-2015/proceedings/EMNLP-Tutorials/pdf/EMNLP-Tutorials06.pdf
2
http://aigicrv.org/2016/
3
https://aclweb.org/anthology/W/W12/#2100
4
https://aclweb.org/anthology/W/W13/#1100
5
https://aclweb.org/anthology/W/W14/#1300
6
http://www.aclweb.org/
7
http://microposts2016.seas.upenn.edu/
8
http://eacl2017.org/
9
http://cci.drexel.edu/bigdata/bigdata2017/
Acknowledgments
This book would not have been possible without the hard work of many people. We would like to thank our colleagues at NLP Technologies Inc., the NLP research group at the University of Ottawa, and our students James Webb and Ruining Liu from the University of Southern California. We would like to thank in particular Prof. Stan Szpakowicz from the University of Ottawa for his comments on the draft of the book, and two anonymous reviewers for their useful suggestions for revisions and additions. We thank Prof. Graeme Hirst at the University of Toronto and Michael Morgan from Morgan & Claypool Publishers for their continuous encouragement.
Atefeh Farzindar and Diana Inkpen
December 2017
CHAPTER 1
Introduction to Social Media Analysis
1.1 INTRODUCTION
Social media is a phenomenon that has recently expanded throughout the world and quickly attracted billions of users. This form of electronic communication through social networking platforms allows users to generate its content and share it in various forms of information, personal words, pictures, audio, and videos. Therefore, social computing is formed as an emerging area of research and development that includes a wide range of topics such as Web semantics, artificial intelligence, natural language processing, network analysis, and Big Data analytics.
Over the past few years, online social networking sites (Facebook, Twitter, YouTube, Flickr, MySpace, LinkedIn, Metacafe, Vimeo, etc.) have revolutionized the way we communicate with individuals, groups, and communities, and have altered everyday practices [Boyd and Ellison, 2007].
The broad categories of social media platforms are: content-sharing sites, forums, blogs, and microblogs. On content sharing sites (such as Facebook, Instagram, Foursquare, Flickr, YouTube) people exchange information, messages, photos, videos, or other types of content. On Web user forums (such as StackOverflow, CNET forums, Apple Support) people post specialized information, questions, or answers. Blogs (such as Gizmodo, Mashable, Boing Boing, and many more) allow people to post messages and other content and to share information and opinions. Micro-blogs (such as Twitter, Sina Weibo, Tumblr) are limited to short texts for sharing information and opinions. The modalities of sharing content in order: posts; comments to posts; explicit or implicit connections to build social networks (friend connections, followers, etc.); cross-posts and user linking; social tagging; likes/favorites/starring/voting/rating/etc.; author information; and linking to user profile features.1 In Table 1.1, we list more details about social media platforms and their characteristics and types of content shared [Barbier et al., 2013].
Social media statistics for January 2014 have shown that Facebook has grown to more than 1 billion active users, adding more than 200 million users in a single year. Statista,2 the world’s largest statistics portal, announced the ranking for social networks based on the number of active users. As presented in Figure 1.1, the ranking shows that Qzone took second place with more than 600 million users. Google+, LinkedIn, and Twitter completed the top 5 with 300 million, 259 million, and 232 million active users, respectively.
Table 1.1: Social media platforms and their characteristics
Statista also provided the growth trend for both Facebook and LinkedIn, illustrated in Figure 1.2 and Figure 1.3, respectively. Figure 1.2 shows that Facebook, by reaching 845 million users at the end of 2011, totaled 1,228 million users by the end of 2013. As depicted in Figure 1.3, LinkedIn also reached 277 million users by the end of 2013, whereas it only had 145 million users at the end of 2011. Statista also calculated the annual income for both Facebook and LinkedIn, which in 2013 totalled US$7,872 and US$1,528 million, respectively.
Figure 1.1: Social networks ranked by the number of active users as of January 2014 (in millions) provided by Statista.
Figure 1.2: Number of monthly active Facebook users from the third quarter of 2008 to the first quarter of 2014 (in millions) provided by Statista.
Social computing is an emerging field that focuses on modeling, analysis, and monitoring of social behavior on different media and platforms to produce intelligent applications. Social media is the use of electronic and Internet tools for the purpose of sharing and discussing information and experiences with other human beings in efficient ways [Moturu, 2009]. Various social media platforms such as social networks, forums, blogs, and micro-blogs have recently evolved to ensure the connectivity, collaboration, and formation of virtual communities. While traditional media such as newspapers, television, and radio provide unidirectional communication from business to consumer, social media services have allowed interactions among users across various platforms. Social media have therefore become a primary source of information for business intelligence.
Figure 1.3: Number of LinkedIn members from the first quarter of 2009 to the first quarter of 2014 (in millions) provided by Statista.
There are several means of interaction in social media platforms. One of the most important is via text posts. The natural language processing (NLP) of traditional media such as written news and articles has been a popular research topic over the past 25 years. NLP typically enables computers to derive meaning from natural language input using the knowledge from computer science, artificial intelligence, and linguistics.
NLP for social media text is a new research area, and it requires adapting the traditional NLP methods to these kinds of texts or developing new methods suitable for information extraction and other tasks in the context of social media.
There are many reasons why the “traditional” NLP are not good enough for social media texts, such as their informal nature, the new type of language, abbreviations, etc. Section 1.3 will discuss these aspects in more detail.
A social network is made up of a set of actors (such as individuals or
