Twitter is “a real-time information network that connects you (users) to the latest information about what you (they) find interesting.”

Twitter has many features similar to other online social networking sites, such as directed social connections between users and status updates; however, unlike other social network services it does not require mutual acquaintance between members for any information to be shared. Unless the account is protected, once a user posts a message on his/her Twitter timeline, that message (tweet) becomes public and can be viewed by anyone. Because of this anonymity and the lack of mutual acquaintance, some scholars claimed that Twitter is not a social network but just an information network.

Figure 10.1 The Most Tweeted Moments of 2012 (tweets per second). Source:

An extensive Twitter study showed that only 22% of the users follow each other, meaning Twitter users in general follow and are followed by different people.3

Users who are me-formers (who frequently send tweets that include “I” “me” “my” etc.) are less likely to be followed, whereas users who are informers (those who have a high reciprocity rate and use words like via, by, RT, etc.) are more likely to be followed.

Along the same lines, people who have Twitter bursts (sending out several tweets within an hour) are less likely to be followed. Additionally, Twitter profiles that are longer and include a location and a URL address are more likely to be followed.4

When it comes to the content, it was found that 90% tweets posted by 10% of users 5 and 85% of trending topics are just headline news.3

In terms of retweetability (possibility that a tweet will be forwarded) the following message categories have a higher chance

  • tweets that have verbs and adverbs
  • tweets that are posted later in the day and posted on Friday, Saturday, and Sunday
  • tweets that have the words you, retweet, and please and
  • news and instructional posts.6

Tweets sent on weekends are more likely to be read or clicked on because

  • commercial entities send fewer messages on weekends, so the visibility of messages sent by real people increases, and
  • on weekends people have more leisure time so they can browse the internet.
Dann, S. (2010). “Twitter content
classification”. In First Monday,
February 2010, 15 (2). [online]:
First Monday. Retrieved Dec. 26,
2013, from December 06, 2010
1. Conversational 2. Pass along
3. News
4. Status
5. Phatic
6. Spam
Naaman, M., Boase, J. & Lai, C.-
H. (2010). Is it Really About Me?
Message Content inSocial
Awareness Streams. In
Proceedings Computer Supported
CooperativeWork 2010,
Savanah: ACM
1. Information sharing
2. Self-promotion
3. Opinions/Complaints
4. Statements/Random thoughts
5. Me now
6. Question to Followers
7. Presence maintenance
8. Anecdote (me)
9. Anecdote (others)
Java, X. Song, T. Finin, and B.
Tseng, 2007. “Why we Twitter:
Understanding microblogging
usage and
communities,” Proceedings of
the Ninth WEBKDD and First
SNA–KDD Workshop on Web
Mining and Social Network
Analysis, pp. 56–65.
1. Daily chatter
2. Conversations
3. Sharing information, URLs
4. Reporting news
Jansen, M. Zhang, K. Sobel, and
A. Chowdury, 2009. “Twitter
power: Tweets as electronic word
of mouth,” Journal of the
American Society for
Information Science and
Technology, volume 60, number
11, pp. 2,169–2,188
2. Sentiments
3. Information Seeking
4. Information Providing
a. Positive/negative comments
b. Response
c. Question
d. Answer
e. Chitchat
f. Suggestion
g. Comment
h. Expecting
i. Patronizing
j. Announcement
k. Request
l. Forwarding
Honeycutt and S. Herring, 2009.
“Beyond microblogging:
Conversation and collaboration
via Twitter,” Proceedings of the
Forty–Second Hawai’i
International Conference on
System Sciences (HICSS–42),
pp. 1–10
1. About addressee (solicit or comment on information)
2. Announce/advertise
3. Exhort (direct/encourage others to do
4. Information for others
5. Information for self 6- Met commentary
(about twitter)
7. Media use
8. Opinion (subjective or evaluative position)
9. Other’s experience
10. Self experience
11. Solicit information
12. others (greetings, nonsense)
Pear Analytics, 2009. “Twitter
study” (August),
1. News
2. Spam
3. Self-promotion
4. Pointless babble
5. Conversational
6. Pass-along value
K. Ehrlich and S. Shami.
Microblogging inside andoutside
the workplace. In ICWSM ’10:
Proc. of the Int.Conf. on Weblogs
and Social Media. AAAI
1. Status (what are you doing now)
2. Information (links, news, comments,
opinions etc)
3. Retweet
4. Questions
5. Directed posts
6. Directed Questions
Table 10.1 What Do People Tweet About?

Personality and Twitter Use 

Personality analyses of Twitter users showed that extroverts post more about family; conscientious (organized) people post more about work, use fewer negatives (no, not) and use more links per tweet; those prone to stress post more about religion; and people who are open use more articles (a, an, the).

Popular users (ones with many followers) and influential users (ones with many followers and whose messages garner responses) tend to be emotionally stable and extroverted.9

Influentials tend to be conscientious and popular users tend to be imaginative. Just like the pattern on Facebook, an analysis of emotions in tweets showed that people post more negative emotion–related words (e.g., annoyed, scared, etc.) later in the day and more positive emotion–related words (e.g. happy, excited, etc.) on weekends.10

Compared to Facebook users, on the other hand, Twitter users tend to be less sociable but have a higher need for cognition (curiosity about things, life, interest in mentally challenging tasks, etc.).11 

Twitter Use during Disasters 

When disasters hit, Twitter becomes an enormously important tool to share and receive information because traditional communication channels (TV, radio, newspapers, and telephones) may not function well as a result of disruption in power lines or supply distribution channels.12

Additionally, people utilize social media channels during and after disasters to get unfiltered and timely information, determine the magnitude of the disaster, check in with family and friends, selfmobilize, seek emotional support, and maintain the sense of community.13

Twitter was successfully utilized by the public during the earthquakes in Chile and China, grass fires in Oklahoma and bushfires in Australia.12

In our study on the Tohoku disaster, we found that many people benefited from social media and one of the victims who was trapped on the roof of a building got help through Twitter.14 However, when a disaster strikes, most people send out tweets to indicate they are safe rather than they are in danger, because the number of people who are safe tends to be higher.

Additionally, some people try to spread false news and rumors12 (this exact motivation of this behavior is not known). After the Tohoku earthquake, many Twitter users affected by the disaster complained about the rumors and suggested either introducing official hashtags or limiting the number of RTs for each hashtag to prevent these problems.12

Hurricane Katrina (2005): About half of the victims used Internet to contact network members that they haven’t interacted with for more than a year.
13 million Americans used Internet to make donations.
Haitian Earthquake (2010): 2.3 million Tweets included the word “Haiti” and raised awareness of the disaster. The Wall Street Journal created a slideshow featuring photographs taken by disaster victims and shared online.
Tuscaloosa & Joplin Tornadoes (2011): People received the first images of the disaster on Twitter. A Facebook group was created to coordinate rescue efforts and locate family members and was joined by more than 120,000 people in a few days. Another social media site was created to collect ideas about how to improve life in the aftermath. More than 300 ideas shared on the site. People found out about volunteer opportunities in social media.
Hurricane Sandy (2012): 1.1 million tweets mentioned Hurricane Sandy during the first 21 hours. Photo-sharing site Instagram received 10 storm related pictures per second. The use of Skype increased by 122% right after the disaster. The photo showing dark clouds over the Statue of Liberty circulated widely but turned out to be a Photoshop trick.
Table 10.2 Examples of Twitter /Other Social Media Usage During Disasters. Source: Faustina et al. (2012)

Twitter & Civic Journalism 

Civic journalism is the term for average people’s contribution to the dissemination of news, and goes back to the Seattle riots of 1999. During those riots, protesters thought the mainstream media portrayed them negatively and founded their own websites to provide objective news.15

Civic journalism may be very helpful for society because anyone can participate in it and it is quick and more effective in places where there is government censorship or regulation (e.g., courtrooms).

On the other hand, using social media, especially Twitter, has some serious downsides, including limited text space, amateur journalism, and low credibility, which spurs hoaxes and rumors.16

Despite the fact that 50% of people found out about breaking news via social media, 49% of social media users state that they heard of at least one news story that turned out to be false.16 Overall, a quarter of journalists agree that crowdsourcing improves journalism, while 40% believe social media is a threat to the objectivity of journalism.17

Even CNN, the operator of the world’s largest citizen journalism platform, with more than one million members (iReporters) worldwide, denigrates its contributors according to a study about Iranian elections.18

Nevertheless, with the help of citizen journalists many real news stories broke on Twitter, including Osama Bin Laden’s death, the Hudson River plane crash, the royal wedding announcement, and Hillary Clinton’s decision to not join Obama’s cabinet.19

Regarding false news, the biggest problem seems to be the lack of interest in forwarding corrections. For instance, during the Occupy Wall Street events, a false news story posted by NBC was retweeted many times. Shortly afterward, both NYPD and NBC corrected the news, but the corrections were retweeted by only a small fraction of those who tweeted the wrong news.20

Chapter References:

1. Twitter (n.d.). Retrieved Dec 26, 2013, from 

2. Cataldi, M., Di Caro, L & Schifanella, C. (2010). Emerging topic detection on Twitter based on temporal and social terms evaluation. In Proceedings of the Tenth International Workshop on Multimedia Data Mining (MDMKDD ’10). ACM, New York, NY, USA. 

3. Kwak, H., Lee, C., Park, H., & Moon, S. (2010, April). What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web (pp. 591-600). ACM. 

4. Hutto, C. J., Yardi, S., & Gilbert, E. (2013, April). A longitudinal study of follow predictors on twitter. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 821-830). ACM. 

5. Heil, B., & Piskorski, M. (2009). New Twitter research: Men follow men and nobody tweets. Harvard Business Review Blog. Retrieved Dec 26, 2013, from Culture and Social Media 183 

6. Zarella, Dan (Oct 28, 2010) The science of Twitter. Web Log Retrieved Dec. 26, 2013, from 

7. Naaman, M., Boase, J., & Lai, C. H. (2010, February). Is it really about me?: message content in social awareness streams. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (pp. 189-192). ACM. 

8. Ehrlich, K., & Shami, N. S. (2010, May). Microblogging Inside and Outside the Workplace. In ICWSM. 

9. Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our Twitter profiles, our selves: Predicting personality with Twitter. In Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom) (pp. 180-185). IEEE. 

10. Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051), 1878-1881. 

11. Hughes, D. J., Rowe, M., Batey, M., & Lee, A. (2012). A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage. Computers in Human Behavior, 28(2), 561-569. 

12. Acar, A., & Muraki, Y. (2011). Twitter for crisis communication: lessons learned from Japan’s tsunami disaster. International Journal of Web Based Communities, 7(3), 392-402. 

13. Fraustino, J. D., Liu, B., & Jin, Y. (2012). Social media use during disasters: A review of the knowledge base and gaps. Final Report to Human Factors/Behavioral Sciences Division, Science and Technology Directorate, US Department of Homeland Security. 

14. Acar, Adam (Mar. 24, 1011). The last tweet before the tsunami hit. Wb Log. Retrieved Dec. 26, 2013, from 

15. Bruns, A., & Highfield, T. (2012). Blogs, Twitter, and breaking news: the produsage of citizen journalism. Produsing Theory in a Digital World: The Intersection of Audiences and Production in Contemporary Theory, 80, 15-32. 

16. Laird, Sam (Apr 18, 2012) .How Social Media Is Taking Over the News Industry. Mashable Web Log Retrieved Dec. 26, 2013, from 

17. Cision & Canterbury Christ Church University (2012). Social journalism study 2012. Retrieved Dec. 26, 2013, from 184 References 

18. Palmer, L. (2012). “iReporting” an Uprising: CNN and Citizen Journalism in Network Culture. Television & New Media. 

19. Strom, Roy (Jul 8, 2011). Factbox: News that broke on Twitter. Reuters Retrieved Dec. 26, 2013, from 

20. Ehrenberg, Rachel (Oct 20, 2012). Social media sway. Science News Retrieved Dec. 26, 2013, from _Media_Sway

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