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The fire was tweeted
1. Anders Lönnermark, SP, 2015-02-11
The fire was tweeted:
Twitter, information flows and the 2015 Channel
Tunnel fire
Dimitrinka Atanasova1, Paul Reilly1 & Alejandra Castaño-Echeverri2
1University of Sheffield, 2University of Leicester
2. Overview
• The Channel Tunnel fire (January 2015)
• Theoretical framework: Affective publics
• Method: Critical thematic analysis; framing analysis,
content analysis
• Results
• Conclusion
Photo credits: from top to bottom (1) Rob Stothard/Getty Images; (2) Leon Neal/AFP/Getty Images; (3) AFP/AFP/Getty Images
3. The Channel Tunnel fire, 17 January 2015
• At 11.25am, French firefighters were mobilised after a fire on a lorry triggered an alarm on
the UK-bound Eurostar train near the French end of the Channel Tunnel.
• Passengers were forced to wear oxygen masks as smoke began to fill their carriage in the
seven minutes before they were evacuated from the train.
• Services resumed on 18 January 2015, but power supply problems unrelated to the fire
caused further cancellations on that and the following day.
• Thousands of passengers were affected by the subsequent disruption to Eurostar services.
• A subsequent investigation revealed that a bolt of electricity from an overhead power line
had caused the fire.
4. Theoretical framework: Affective publics
• The ‘ambient storytelling infrastructure’ of Twitter enables
‘affective publics’ to present their own perspectives on
incidents (Paparachissi, 2015).
• This creates new opportunities for responding agencies -
from the crowdsourcing of crisis information to the
provision of accurate, real-time information to members of
the public.
• This study explored the effectiveness of Eurostar’s
communication strategy, with a specific focus on the role of
journalists and members of the public in the information
flows that emerged during the incident.
6. Tweets
• A critical thematic analysis, derived from the
framework of Braun and Clarke (2006), was
used to explore the key themes in tweets.
• A content analysis (Krippendorff, 2004) was
carried out to classify the users who wrote
the tweets by type and to also categorise
the type of web links that were included in
tweets.
News articles
• A framing analysis, derived from the work of
van Gorp (2010), was used to identify
frames in news articles.
Method
7. News media coverage focused on angry passengers
dissatisfied with Eurostar
Disaster/Desert island frame (being
stuck on an island with no one to
help/no one to communicate with)
CHANNEL Tunnel customers stranded
by a fire on Saturday faced a second day
of misery yesterday. CHAOS
EUR GOING NOWHERE;
Day 2 of Chunnel mayhem
marooned Eurostar passengers
8. Some Twitter users also expressed frustration at lack of
information provided by Eurostar
Twitter users expressed frustration at what
they saw as insufficient advice from Eurostar
staff:
stuck @Eurostar without any proper info.
Very bad crisis management
Advice on @Eurostar website & twitter not
consistent with reality.
Massive #eurostar fail: 1/2 hour estimated
waiting time on the phone #batterylife
@Eurostar you never replied my tweets How
the hell am I supposed to get home?
@Eurostar this is so frustrating - we are
getting no updates. Any updates Eurostar?
I don’t believe you will be ‘with you as soon as
we can’ @Eurostar" #answerthefuckingphone
@Eurostar answer the phone !
#answerthefuckingphone
Hello @Eurostar. Please answer the phone
and my tweets.
@Eurostar Google Translate does a better job
of writing in plain English. Try it.
@eurostar stop giving misleading info.
9. Other Twitter users praised the professionalism of
Eurostar staff for responding promptly to queries
Cheers @Eurostar for being very helpful and
informative!
Annoying that the trains are suspended but
absolutely excellent customer service from
@Eurostar.
@Eurostar - despite the delay - your staff stays
calm and its communication is perfect.
... Impressed with @Eurostar service under
difficult circumstances. Very polite &
informative announcements.
That said, their social media team are doing a
great job on keeping everyone up to date
with what they know. Well done @eurostar
@Eurostar Thank you for clarifying and for
your efforts to get info out there!
My compliments to @Eurostar who have kept
me well informed over the past 24 hours
10. Media was criticised for creating a ‘non-story’ revolving
around angry passengers
@bbc5live BOO Marvellously negative
Eurostar travellers!!
@BBCBreaking @BBCNews Can you please
stop endlessly taking about the non story
today about #Eurostar. #notnews
there does seem to be bbc bias against
Eurostar. Do you report on plane
cancellations?
Dear BBC. Eurostar story is NOT headline
news.
@Eurostar re BBC quote. I said slowed all the
way down. Not smoke all way down. No
smoke. Have asked @BBCBreaking to correct
The media are trying to create another event
#channeltunnel
@BBCBreaking Keep interviewing passengers
and eventually one is bound to criticise
Eurostar?
11. Information flows originated from the Twitter accounts
of eyewitnesses rather than organisations
• Twitter accounts belonging to individuals
rather than organisations tended to be
responsible for starting information flows
about the Channel Tunnel fire.
• The first tweet reporting the incident,
according to our data, came from an
individual user:
17/01/2015 09:27:45 Our Eurostar just
stopped in the middle of nowhere for ‘safety
reasons’ on our way to London
46%
35%
19%
Individuals Other Organisations
The pie chart shows what share of the total number of 12,596 analysed tweets were authored by individuals, organisations or other
users (e.g. bots).
12. Professional journalists/news media outlets dominated
information flows
• After excluding tweets authored by bot
accounts; users whose identity could not be
determined (e.g. due to insufficient details
in their Twitter bios); and users who listed
multiple affiliations (e.g. ‘journalist at XX,
founder of YY and student at ZZ University’),
accounts of journalists/news media outlets
accounted for the largest share of the total
number of tweets.
45%
34%
20%
1%
Media/rep Company/rep
Blog/ger Government/rep
The pie chart shows what share of the total number of 12,596 analysed tweets were authored by media/rep, company/rep, blog/ger
or government/rep accounts. Note that the large number of company/reps is partly due to the composition of Eurostar customers.
13. Most tweets link to coverage of the fire on news media
outlets’ websites
• There were 5,596 tweets (47.6% of all
tweets) which contained at least one web
link. 4,701 of these tweets contained a web
link which pointed to the website of various
traditional news media outlets - mostly of
newspapers, but also of television channels.
• Here ‘video’ refers mainly to YouTube
videos, ‘photographs’ to Instagram images,
‘other’ to websites like msn and yahoo. Due
to link decay, there were cases in which user
type could not be determined.
4,701
768
464
223 96 48 43 20 10
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
The pie chart shows what share of the total number of tweets which contained at least one web link (5,596) pointed to a traditional
news media website, a company website, etc.
14. Conclusion
1. News media coverage of the incident focused on angry passengers dissatisfied with Eurostar.
2. Similarly, some tweeters expressed their frustration at what they saw as the insufficient and
contradictory advice they had received from Eurostar staff.
3. However, there were positive tweets expressing gratitude for the professionalism of the
company and their prompt reply to customer queries.
4. Some tweeters were critical of the media coverage of the incident for its focus on angry
passengers dissatisfied with Eurostar.
5. Twitter appeared to provide a platform for a diverse ‘affected publics’ that were not fully
represented in the news media coverage of the incident, but nevertheless played a crucial role in
sharing information about the incident online.
15. References
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in
Psychology, 3(2), 77-101.
Krippendorff, K. (2004). Content analysis: An introduction to its methodology (2nd ed.).
Thousand Oaks, CA: Sage.
Paparachissi, Z. (2015). Affective publics: Sentiment, technology and politics. New York: Oxford
University Press.
van Gorp, B. (2010). Strategies to take subjectivity out of framing analysis, In d’Angelo, P. &
Kuypers, J. (ed) Doing news framing analysis: Empirical and theoretical perspectives. New York:
Routledge, pp. 84-109.
16. Acknowledgements
‘CascEff: Modelling of dependencies and cascading effects for
emergency management in crisis situations’ (Grant
agreement no: 607665) is performed and funded under the
Seventh Framework Programme (FP7) of the European Union
(SEC-2013.4.1-2).
