Post on 28-Nov-2014
description
transcript
“The evolution of social media into a robust mechanism for social transformation is already visible. Despite many adamant critics who insist that tools like Facebook, Twitter, and YouTube are little more than faddish distractions useful only to exchange trivial information, these critics are being proven wrong time and again.”
Simon Mainwairing
The meteoric growth of social media
Source: PEW Internet & American Life Project, 2012
Source: PEW Internet & American Life Project, 2012
The meteoric growthof social media
Why study social media?
• Impact of Web 2.0 media tools on different fields of activity:
• Political communication;• 2006: 16% of major party congressional
contenders on Facebook (U.S. Midterm elections);
• 2008: 79% of major party House hopefuls on Facebook (U.S. Presidential elections);
• 2010: 82% of major party congressional candidates on Facebook before elections (U.S. Midterm elections).
• Advertising;• Online dating (e.g.: “Spotted at” pages on
Facebook);• Journalism;• Etc.Sources; Williams and Gulati, 2009; Johnson and Perlmutter, 2009; Lariscy, Avery et al., 2009; Wiilliams and Gulati, 2011;
Why study social media?
• The impact of Web 2.0 media tools on public relations:• Social media as moved from “from ‘buzz word’
status to strategic tool” for PR specialists;• Results from a survey of 273 U.S.-based PR
practitioners in 2011:• Between 0 and 35 years of experience;• Between 21 and 61 years-old (median age: 40);• 35 per cent reported using Twitter in the 15
mins before taking the survey;• 51.2 per cent reported using Twitter in the
hour before taking the survey;• 90 per cent reported using Twitter in the 24-
hour window before taking the survey.
Sources; Sweetser and Kelleher, 2011; Eyrich, Padman et al.,2008
Challenges and opportunities of social media research
• What is the research question?• Identification of one or multiple research problems;• Ethical considerations;• Importance to have a basic understanding of social
media (in other words, play with these tools!):• Structure;• Functionalities;• Etc.
• What are the research objectives?• Identification of one of multiple research hypotheses:
• Can it be done?• Can it be verified?• Can it be replicated?• Etc.
• Identify limitations.Source: Raynauld, Giasson et al., 2011
Challenges and opportunities of social media research
• Adapting your approach (flexibility):• Two main functions: (1) content dispersion and
(2) social interaction;• Social networking services share three broad
traits:• Users can create and manage profile pages
within the confines of an established system;• Users can display their connections with other
users within the established system;• Users can access and browse through “their list
of connections and those made by others” within the established system.
• They all have distinct properties (e.g.: “like” function on Facebook;
• Constantly-evolving nature of social media channels. Sources; Boyd and Ellison, 2007; Papacharissi and Gibson, 2011
Challenges and opportunities of social media research
• Data mining and archiving (sampling):• Selection of a sampling tactic (in the case of Twitter):
• Monitoring accounts;• Monitoring tweets:
• Keywords;• Social interaction mechanisms (e.g.:
@retweets, @mentions, etc.);• Hashtags;• Hyperlinks;• Etc.
• Data mining and analysis technology;• Hardware;• Software (importance of open-source tools).
• Flexibility now “the name of the game”.
Sources; Raynauld, Giasson et al., 2011; Hemphill, Shapiro et al., 2012; Zappavigna, 2011; Mascaro, Black et al., 2012
Challenges and opportunities of social media research
Sources; Krishnamurthy and Wills, 2009; Small, 2011; Fitton, Gruen et al., 2009
• Selection of an analytical approach (in the case of Twitter):
• Users:• Presence of “personally identifiable
information” (PII);• Verifiability of information provided.
• Digital material:• Text (140 characters);• Hyperlinks (often shortened);• Multimedia (e.g.: twitpic, instagram, keek,
etc.);• Etc.
• Concerns with interpretation:• Classifying content by associating it to specific
issues, events, etc.;• Coordinating decentralized conversation;• Issuing comments or opinions.
Case study: #teaparty
• What is the Tea Party movement?• Mainstream emergence in February 2009;• Essentially leaderless;• Research hypotheses:
• Decentralized organizational structure;• Hyper-fragmented interests.
• Quantitative content analysis of #teaparty discourse on Twitter:
• Tweets with #teaparty hashtag posted between December 9, 2009 at 22h41 +0000 and March 19, 2011 at 15h40 +0000 (Midterm elections);
• Twapper Keeper for data mining and archiving (open-source);
• MySQL and Gephi (version 0.8.1 beta) for data analysis.
Sources; Raynauld, 2013
Case study: #teaparty
• Overview of the #teaparty tweeting dynamic:• 1,747,306 tweets with at least one #teaparty
hashtag;• 79,564 unique twitterers;• 96.64 per cent of the #teaparty tweets with all
the correct information (technical issue affecting 3.36% of the dataset).
Source: Raynauld, 2013
Case study: #teaparty
Decem
ber 2
009
Janu
ary 20
10
Febr
uary
2010
Marc
h 201
0
April
2010
May
2010
June
2010
July
2010
Augus
t 201
0
Sept
embe
r 201
0
Octobe
r 201
0
Novem
ber 2
010
Decem
ber 2
010
Janu
ary 20
11
Febr
uary
2011
Marc
h 201
1
0
50,000
100,000
150,000
200,000
250,000
300,000
51,197
68,204
60,405
47,787
50,349
156,680
129,215
174,582
181,122204,575
275,408
198,596
42,35733,700
3,113
11,391
Monthly volume of #teaparty tweets (per number of tweets)
Source: Raynauld, 2013
December
2009
January
2010
Febru
ary 2010
March 2010
April 2010
May 2010
June 2010
July 2010
August
2010
Septem
ber 2010
October
2010
November
2010
December
2010
January
2011
Febru
ary 2011
March 2011
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
Monthly number of unique twitterers who contributed at least once to the #teaparty conversation (per number of twitterers
Source: Raynauld, 2013
Case study: #teaparty
Source: Raynauld, 2013
• 85,629 @replies (4.9% of the dataset) by 11,296 unique #teaparty twitterers;
• 578,939 #teaparty tweets (31.13% of the dataset) by 54,802 unique users served a retweeting function;
• 1,179,742 #teaparty tweets (67.52% of the dataset) by 54,534 unique authors featured at least one hyperlink.
Case study: #teaparty
Source: Raynauld, 2013
Number of @replies
877
Number of nodes
654
Number of edges
648
Average degree 0.991
December 14, 2009 to
December 20, 2009
Case study: #teapartyNetwork analysis
Source: Raynauld, 2013
Case study: #teaparty
Number of @replies
4,424
Number of nodes
3,258
Number of edges
3,542
Average degree 1.087
November 1, 2010 to
November 7, 2010
Network analysis
Source: Raynauld, 2013
Case study: #teaparty
Number of @replies
4,280
Number of nodes
2,630
Number of edges
3,131
Average degree 1.19
October 25, 2010 to October 31,
2010
Network analysis
Source: Raynauld, 2013
Case study: #teaparty
Number of @replies
688
Number of nodes
807
Number of edges
624
Average degree 0.773
January 10, 2011 to January 16,
2011
Network analysis
Source: Raynauld, 2013
Case study: #teaparty
• 49,797 different hashtags (including the #teaparty hashtag) used by #teaparty twitterers;
• 1,747,306 #teaparty tweets with at least one hashtag;• 178,417 different hashtag combinations (hyper
fragmentation;• 10 most popular hashtags:
1- #teaparty2- #tcot3- #p2 4- #sgp5- #gop
6- #tlot 7- #ocra 8- #912 9- #twisters 10- #iamthemob
Conclusion
• Social media platforms are now an integral component of the modern media environment;
• Redefinition of traditional research approaches:• Mass media versus participatory media;• Top-down versus multidirectional information
flows and social interactions;• Constantly-evolving nature of social media (e.g.:
Facebook timeline);• Etc.
• The rise of “big data” research• Research shifting into a “perpetual beta” mode?• More “research on research” required.
QUESTIONSor
COMMENTS