Abstract
videos, music, and even games on Facebook, Twitter, Instagram, Flickr, Pinterest, Snapchat, YouTube, and Vine, continues to grow exponentially. Despite their abundant presence, and the wealth of insight that social media researchers could obtain from them, few methods have been developed and utilized to use them. They are naturalistic, “found” data sources, just as
tweets and blog posts are, but they are frequently ignored in favour of text-based data. The purpose of this paper will not present original empirical results; instead, it is meant to introduce social media researchers to potentially new data sources as well as methods for analysing them. Results from the author’s previous studies in this area will be used as examples.
| Original language | English |
|---|---|
| Pages | 1-3 |
| Number of pages | 3 |
| Publication status | Accepted/In press - 7 Mar 2016 |
| Event | Social Media and Society - London, United Kingdom Duration: 11 Jul 2016 → 13 Jul 2016 |
Conference
| Conference | Social Media and Society |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 11/07/16 → 13/07/16 |
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Keywords
- social media
- data analysis
- non-text data
Cite this
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Analysing found non-text social media data : options and challenges. / Pennington, Diane.
2016. 1-3 Abstract from Social Media and Society, London, United Kingdom.Research output: Contribution to conference › Abstract
TY - CONF
T1 - Analysing found non-text social media data
T2 - options and challenges
AU - Pennington, Diane
PY - 2016/3/7
Y1 - 2016/3/7
N2 - This paper is based on a chapter entitled “Coding of non-text data” (Rasmussen Pennington,in press) that has been accepted for publication in The SAGE handbook of social media research methods. The chapter outlines the special concerns associated with collecting and analyzing data found on social media sites and not in language-based text (Rasmussen Neal, 2012). The presence of non-text information on social media sites, such as photographs,videos, music, and even games on Facebook, Twitter, Instagram, Flickr, Pinterest, Snapchat, YouTube, and Vine, continues to grow exponentially. Despite their abundant presence, and the wealth of insight that social media researchers could obtain from them, few methods have been developed and utilized to use them. They are naturalistic, “found” data sources, just astweets and blog posts are, but they are frequently ignored in favour of text-based data. The purpose of this paper will not present original empirical results; instead, it is meant to introduce social media researchers to potentially new data sources as well as methods for analysing them. Results from the author’s previous studies in this area will be used as examples.
AB - This paper is based on a chapter entitled “Coding of non-text data” (Rasmussen Pennington,in press) that has been accepted for publication in The SAGE handbook of social media research methods. The chapter outlines the special concerns associated with collecting and analyzing data found on social media sites and not in language-based text (Rasmussen Neal, 2012). The presence of non-text information on social media sites, such as photographs,videos, music, and even games on Facebook, Twitter, Instagram, Flickr, Pinterest, Snapchat, YouTube, and Vine, continues to grow exponentially. Despite their abundant presence, and the wealth of insight that social media researchers could obtain from them, few methods have been developed and utilized to use them. They are naturalistic, “found” data sources, just astweets and blog posts are, but they are frequently ignored in favour of text-based data. The purpose of this paper will not present original empirical results; instead, it is meant to introduce social media researchers to potentially new data sources as well as methods for analysing them. Results from the author’s previous studies in this area will be used as examples.
KW - social media
KW - data analysis
KW - non-text data
UR - http://socialmediaandsociety.org
M3 - Abstract
SP - 1
EP - 3
ER -