Misinformation in a riot: a two-step flow view

DOIhttps://doi.org/10.1108/OIR-09-2015-0297
Pages438-453
Date14 August 2017
Published date14 August 2017
AuthorNatalie Pang,Joshua Ng
Subject MatterLibrary & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management
Misinformation in a riot:
a two-step flow view
Natalie Pang and Joshua Ng
Wee Kim Wee School of Communication and Information,
Nanyang Technological University, Singapore
Abstract
Purpose Misinformation can have lasting impacts in the management and control of a public emergency.
The purpose of this paper is to demonstrate how misinformation flows and how user characteristics can
shape such flows in the context of a violent riot in Singapore.
Design/methodology/approach The authors apply the two-step flow theory and discuss the mixed
methods approach involving wrangling Twitter data and descriptive analysis to develop and analyse two
corpuses of misinformation related to the riot.
Findings The findings are mostly consistent with the two-step flow theory, in that misinformation flows to
the masses from opinion leaders (as indicated by higher measures such as online social influence and
followers/following ratio). In the presence of misinformation, tweets opposing such misinformation may not
always come from opinion leaders.
Practical implications The authors work furthers knowledge about how misinformation goes viral,
which provides practical implications to help policymakers and scholars in understanding and managing the
dynamics and pitfalls of misinformation during an emergency.
Originality/value This paper tackles the problem of misinformation in public emergencies using a mixed
methods approach and contributes to ongoing theoretical work on managing online misinformation especially
in public emergencies and crises.
Keywords Twitter, Misinformation, Emergency information, Two-step flow
Paper type Research paper
Introduction
Over the last decade, the emergence of social media platforms such as Twitter and Facebook
has resulted in significant changes in the creation, use and consumption of information,
especially during crises and emergencies. During the Haiti earthquake in 2010, Cable News
Network broadcasted pictures of the earthquake which were first available via Twitter and
Facebook users (Simon, 2010). In 2013, news of the Boston Marathon Bombing appeared on
Twitter only seconds after the explosions. Mainstream media such as The Boston Globe
seized the opportunity and reported the incident closely using Twitter. The move increased
their influence: a median number of retweets (RT) per tweet during the incident was 224 as
compared to only four before the bombing. Only five days after the bombing, their Twitter
followers increased by 290 per cent (Rogers, 2013). For mainstream news platforms that are
able to capitalise and make use of information on social media, their news reporting abilities
are not only enhanced but also become more influential during crises, disasters, and other
emergencies (Rogers, 2013).
But the quality of information on social media can be questionable. When wrong
information is created and spread during emergencies, pressing demands for information in
great uncertainty can deter a journalists ability to verify information before reporting it.
Although scholars have been studying the topic of rumours and misinformation
for decades, much of the work is limited to psychology and sociology (Allport and Postman,
1947; Rosnow and Fine, 1976). Terms like rumourand misinformationare used like
synonyms in recent work (Ratkiewicz et al., 2011), but rumours can be both misinformation
and disinformation, with the latter intended to mislead as opposed to misinformation being
unintended. Online, it is not possible to tell if the false information is intended, but for the
purpose of our paper we will use the term misinformation.
Online Information Review
Vol. 41 No. 4, 2017
pp. 438-453
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-09-2015-0297
Received 7 September 2015
Revised 22 January 2016
Accepted 26 February 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
438
OIR
41,4
With the emergence of misinformation on social media, especially during public
emergencies, there are new problems to address. The first problem is that the dissemination
of misinformation can mislead, create panic, and make it even harder to manage a public
emergency. As such the first goal of our study is to further understanding of how
misinformation is disseminated and flows on social media. In our review of the literature,
we also found that while previous work focusses on the characteri stics of the
misinformation, they do not always consider user characteristics. This is understandable,
as such information have not been available or accessible in the past. However, we argue
that the problem of misinformation cannot be addressed until we clarify issues with user
characteristics and behaviours associated with such misinformation. With the availability
of user-related information online, it is now possible to investigate how user characteristics
may contribute to the dissemination of misinformation. This is the second goal of the study.
Background
On 8 December 2013, a riot broke out in Little India, an ethnic suburb in Singapore. It was
only the second violent riot in Singapore in 40 years. The Little India riot was triggered by a
fatal accident involving a bus and an Indian national foreign worker. The emergency
resulted in injuries to rescue and police personnel as well as damages to their vehicles.
Before the Singapore Police Force (SPF) released an official statement and long before the
mainstream media was involved, Twitter contained many reports, sightings, pictures,
videos, and speculations. In Singapore, the sightings of the riot at Little India first appeared
on Twitter at 10:39 p.m. in the form of a picture (@HappyScones, 2013). Shortly after,
TODAYonline,The Straits Times,Channel 8 News, and other mainstream media
organisations requested to repost the picture through their respective channels. Subsequent
reports in traditional media included various YouTube videos, Instagram shots, and Twitter
updates as sources. Such interactions are reflective of partnerships between mainstream
and alternative media platforms (Veil and Ojeda, 2010), which may also mitigate standing
issues of trust between media agencies.
Many tweets in the first hours of the riot gave untrue reasons for the riot. They were picked
up by Channel NewsAsia in Singapore and subsequently by The Guardian (Press, 2013).
Although some of the misinformation was corrected the very next day (The New Paper,2013),
other information which were eventually found to be untrue was on Twitter for many days
(Channel NewsAsia, 2013). In the aftermath of the Little India riot, more rumours circulated on
Twitter, and, again, mainstream media reported it, although they were also retracted eventually.
This turn of events is not unique to the riot that happened in Singapore. After the
Boston Marathon bombing in 2013, Twitter contained misinformation about the attack
(Starbird et al., 2014). Starbird et al. (2014) found that even though such misinformation can be
corrected by other tweets, they are undermined by propagations of the misinformation.
Our study examines these propagations by examining how they flow within social networks.
The subject of our analysis is Twitter and before going further it is important to clarify
its structural features and characteristics. Messages posted on Twitter are called tweets,
limited to 140 characters, which are mostly public but can also be set as private. The act of
posting a message on Twitter is called tweeting. A person who tweets is called a Twitterer
or simply a user. Every Twitterer has a unique username, also known as a Twitter handle.
In the context of the many social media platforms that have emerged over the years,
Twitter is a social networking site with a directed and non-reciprocal friendship model
(Kwak et al., 2010).A Twitterer can follow whomeverthey wish, and those they follow do not
have to follow them back.When one Twitterer follows another,the latters tweets will appear
in the formersTimeline. Timeline is a space wheretweets are displayed in chronological order
for user consumption.Twitter search tools enable discoveryof public tweets even if one is not
following another. To send tweets privately, direct messageis used.
439
Misinformation
in a riot

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