During crisis events individuals look for information and try to share useful content or testify their own experience through social media sites. The research for valuable information is, usually, largely based on information provided - through social media as well as through more traditional media - by news agencies and official actors. This collective behavior leads, on a given amount of time, toward the emergence of gatewatching activities where digital media are usually used to reshare and to control information. But how does this phenomenon emerge? This paper will investigate this specific topic looking at the Twitter conversations produced during the first five hours after the earthquake that struck Emilia Romagna region in Italy on May 20th 2012. By focusing on the first 5 hours of the Twitter stream we have been able to detect the early user-led phase of the phenomenon, showing which type of users has been the first to fill the information gap and, by then, what happened until the early morning when traditional media came on stage. The research has been based both on the a textual qualitative analysis of the tweets, aimed at investigating what kind of messages were produced and by what kind of users, and on a Social Network Analysis of the #terremoto hashtag that showed how user-produced communication results in different network structures than news agencies’ produced ones.

1. IR14: Resistance and Appropriation
The genesis of Crisis Communication in Twitter:
from Witnesses to Gatewatchers
Luca Rossi, IT University of Copenhagen
Elisabetta Zurovac, University of Urbino Carlo Bo
Giovanni Boccia Artieri, University of Urbino Carlo Bo

2. Background and open research questions

3. RQ: How is Twitter used during the outbreak of
a natural disaster?

4. Dataset & data acquisition
Dataset:
#terremoto hashtag
May 20th from 4:00 am to 9:00 am
24121 tweets
Data acquisition:
YourTwapperkeeper

5. Data description
Tweets: 24121
Unique users: 11219
Retweets: 13252
Reply messages: 1819
Link to external resources: 8067

6. What kind of activity?
Ratio between RT and tweet increases form 0,45 during
the first hour to 0,55 in the fifth hour.

7. Temporal perspective

8. The genesis of the gatewatchers

9. Textual analysis
Coded Tweets 21937 Tweets
Tell: tweets with narrative content tied to personal experience and thoughts;
Information: tweets with different level of informative content;
Ask: tweets containing requests for information;
Tweet Consciousness, tweets with meta-comments on Twitter and its functions o
usages;
Explainations: tweets containing theories, hypothesis on the earthquake;
Ironic, tweets making use of irony;
Complaints, tweets complaining about the institutions and mainstream media.

10. Textual analysis
Coded Tweets 21937 Tweets
Tell: tweets with narrative content tied to personal experience and thoughts;
Information: tweets with different level of informative content;
Ask: tweets containing requests for information;
Tweet Consciousness, tweets with meta-comments on Twitter and its functions o
usages;
Explainations: tweets containing theories, hypothesis on the earthquake;
Ironic, tweets making use of irony;
Complaints, tweets complaining about the institutions and mainstream media.

11. Textual analysis
Coded Tweets 21937 Tweets
Tell: tweets with narrative content tied to personal experience and thoughts;
Information: tweets with different level of informative content;
Ask: tweets containing requests for information;
Tweet Consciousness, tweets with meta-comments on Twitter and its functions o
usages;
Assumptions: tweets containing theories, hypothesis on the earthquake;
Ironic, tweets making use of irony;
Complaints, tweets complaining about the institutions and mainstream media.

12. 41%
28%
23%
14%
3%
0%
1%
3%
4%
5%
9%
5%
4%
0%
3%
7%
8%
6%
2%
07:00 - 08:00
2%
73%
12%
56%
06:00 - 07:00
4%
11%
05:00 - 06:00
04:00 - 05:00
Textual analysis
54%
20%
1%

13. Textual analysis
80%
70%
1
2
3
4
5
60%
50%
40%
30%
20%
10%
0%
prima ora
seconda ora
TELL
terza ora
INFORMATION
quarta ora
ASK
COMPLAINTS
quinta ora

14. Users analysis

15. Users analysis
Degree Centrality
Betweenness Centrality

16. Thank you
http://www.snsitalia.it
Luca Rossi – lucr@itu.dk
Elisabetta Zurovac – elisabetta.zurovac@uniurb.it
Giovanni Boccia Artieri – giovanni.bocciaartieri@uniurb.it