Twitter as a Social Reporting Tool under Crisis Events: Two Studies

K. Hazel Kwon, PhDCulture and Communication

Drexel University

Organization of the Presentation1. Presenting a preceding study “Rumor

theory and Twitter during the Haiti Earthquake 2010”: To contextualize how the main research began

2. Presenting the main study “The second-level gatekeeping and content concentration in Twitter: A temporal analysis of Gaza conflict 2009”

2011 Japan earthquake &

Tsunami:• 8.9

• 15,844• 5,890• 3,451

•A number of nuclear accident

QUAKEBOOK: • A Twitter-sourced

charity book • Essays about the

moment of earthquake

• 100% profits sent as donation

“Day of Revolt 2011” in Egypt

• 2 millions at Tahrir Square

• The downfall of Murabak

• The April 6 Youth Movement on Facebook

“We use Facebook to schedule the protests, Twitter to coordinate,

and YouTube to tell the world.”

“Ushahidi Project”

• Al-Jazeera collaborated with Ushahidi during

Gaza-Israel conflict.

• “Networked Journalism”

• Citizens use mobile media to report violence, death,

protest, international aid, rape etc.

What are in Common?•Strategic communication (i.e. goal-

oriented, intend to change attitudes or behaviors or mobilize actions).

•Communication exchanges on a global scale.

•Use mobile media and the Internet.•Use the recent web applications (e.g.

Facebook, Twitter, Aggregating web services): Social Media based

strategic communication

The preceding study: Twitter during Haiti Earthquake 2010• A project about the use

of social media as a social reporting tool under natural disasters.

• The Haiti paper looked at the process of ambiguous message exchanges (rumors) developing into strategic communication and social collaboration.

The preceding study: Twitter during Haiti Earthquake 2010• The key finding was …

• Rumors are reduced by adding credence to information.

• Credible information helps users understand the situation correctly and coordinate actions to solve problems.

To reduce rumors…

Anxiety

Information Ambiguity

Rumors

To reduce rumors…

Anxiety

Information Ambiguity

Rumors

To reduce ambiguity, the process to

authenticate information is

necessary!

1) Aim of the study To understand the process of information

authentication and how it led to the emergence of collaboration in Twitter during Haiti Earthquake.

2) Methods• Tweets from Jan 12th to Jan 21st, 2010.• Search hashtags (#): HaitiEarthquake, HaitiQuake,

HaitiHelp.• Contents were categorized into three types of

statements: Ambiguous, Authenticating, and Strategic

Haiti Earthquake 2010

Statement Description Examples

Ambiguous •Emotionally charged statements •Questions and comments without supporting materials

•“My soul is deeply sad” •“Which relief agencies donate aid to Haiti?”•“I have no idea if it’s true or not”

Authenticating

•Add credence to what the user says•Citing reputed source or references to self /others as an expert on something

•“CNN reporting a further 2 aftershocks in Haiti mag 5.9 and 5.5”

Strategic •Statements that suggest a course of action

•“Please RT to help the victims of today's earthquake!”

Statement Categories

Stage 1 Stage 2 Stage 3 Stage 4

Ambiguous

Authenticating

Haiti Earthquake 2010: Change of Twitter communication over time

Stage 1 Stage 2 Stage 3 Stage 4

Ambiguous

Authenticating

Strategic

Haiti Earthquake 2010: Change of Twitter communication over time

Stage 1 Stage 4

Pentagon: authenticating words; Triangle: strategic words

Haiti Earthquake 2010: Semantic Network Analysis

Conclusions from the study…

• Information authentication is the most prominent communication process under the crisis event.

• It’s enacted by lending credence to what users say.• Credence is gained by reliable sources such as

links to pictures, mainstream media, or well-known organizations.

• In other words, by citing external contents outside Twitter.

• Sharing external content results in re-circulating/re-distributing existing online contents.

Sharing preexisting online content

• A significant part of Twitter practice, especially to use the media for strategic communication.

• Majority previous studies assume Twitter users as content creators rather than distributors.

• Distribution is not merely a passive consumption: the practice is enacted based on users’ decision making within the established media dynamics.

Main Study:

A Temporal Analysis of User-Selected Contents in Twitter during the 2009 Gaza Conflict

Contributions of the study

• Theoretically…

Introduce a concept of “second-level gatekeeping” adapting social media environment (i.e. Twitter).

Understand the distribution pattern of online news contents in social media (i.e. Twitter)

• In practice…

Strategic use of Twitter does not occur in a vacuum. One of the early studies to discuss the utility of social

media as a carrier of citizen engagement within the constrains of preexisting media dynamics.

Traditional Model of Gatekeeping

• “The process by which selections are made in media work, especially decisions whether or not to admit a particular news story to pass through the ‘gates’ of a news medium” (McQuail, 1994, p.213)

• Traditional model… - A series of filtering mechanisms within the provider’s

system. -The role of receiver was considered not as a part of

processing but as a final destination after the processing.

- Traditional model is not comprehensive in the user-centric social media environment.

The ‘Second-level’ Gatekeeping

• The “Gated” (Barzilai-Nahon, 2008): users intervene in gatekeeping mechanism by…

1. participating in producing new information 2. participating in circulating already existing

information The second-level gatekeeping Decision-making on what to select over other alternatives: Processed news products are just one option among all types of resources on the Web Reconstructing information by adding the user’s own commentUsually enacted by linking URL to a post

The ‘Second-level’ Gatekeeping

• The “Gated” (Barzilai-Nahon, 2008): users intervene in gatekeeping mechanism by…

1. participating in producing new information 2. participating in circulating already existing

information the second-level gatekeeping decision-making on what to select over other alternatives: processed news products are just one option among all types of resources on the Web reconstructing information by adding the user’s own commentusually enacted by linking URL to a post

Second-level Gatekeepers (SG)vs. Opinion Leaders (OL)• Incorporates the “two-step flow” model (Katz,

1957) into the gatekeeping theory• OL in two-step flow: receive information from the

elite media and influence on interpersonal networks• Differences between OL and SG… 1) recipients of vs. participants in news production 2) influence only their social circles vs. including

unknown strangers online 3) verbal conversation vs. textual reporting

(editable & reproducible)

Second-level Gatekeeping in Twitter does NOT occur in a vacuum• Affected by the established media dynamics

(Napoli, 2008, p.57)

• Online Realm - the semblance of openness and lowered entry

barrier. - not ideally decentralized. - audience attention clustered around

corporatized online services and the off-to-online presence of traditional mass media realm.

- “Power-law” distribution

As diverse & decentralized as we conveniently assume?

• Some critics against the decentralized web… 1. Sunstein (2001): Self-regulation prevents

comprehensive info. adoption. 2. Mitchelstein and Bockzkowski (2010) : No

radically different news consumption habit from offline.

3. Meraz (2009): Elite journalism and celebrity bloggers hold he blogsophere, leading the unequal structure, following “power-law” model.

4. Hindman (2009): Political use of the Internet shows an unequal structure, following “power-law” model.

HypothesesH1: Twitter users’ selection of news

contents will collectively produce a power-law structure, representing the uneven representation among the available content providers.

0 5 10 15 20 25 30 35 40 450

20406080

100120140160180200

k = a frequency of being tweetedP(K

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HypothesesWhy unequal structure?

Twitter’s participatory potential is confined by…

1) The existing web infrastructure (Hyperlink structure)

H2: The hyperlinks structure configured on the general Web will be positively associated with the frequency at which Twitter users select a particular online content for redistribution.

Hypotheses

2) Traditional media force: - Item diversity does not necessarily lead to exposure

diversity (Yim, 2003). - Twitter users will be more likely to choose contents

created by traditional media realm due to their familiarity to its format and channel royalty, and the relative mass appeal of the high-budget products

H3: Twitter users’ content selection will be concentrated more to the traditional media realm than other alternative forms of contents.

Hypotheses

3) Interaction between the two? - Mass media content may be preferred when the

website gains popularity thus is ranked on top from the search results.

- Even though search result presents a website in a higher-order, it may not be considered as the most relevant if the source site were never heard previously

H4: Twitter users’ content selection will be influenced by the interaction effects between content types and hyperlinks structure.

Temporal Analysis•Selection of news contents can also be

contingent on the news lifespan. •Although a conflict is an instantaneous

incident at the moment when it breaks out, a more complex political agenda can be unveiled as the news is progressed.

RQ1: Is there any difference in Twitter news selection according to the news lifespan?

Retweeting• Purposive practices• Facilitates rapid information diffusion within

Twitter• Create collective minds among the users of

shared interests• Two types of retweeting: (1) Retweeting external content (2) Retweeting internally generated content : a

special case of second-level gatekeeping in Twitter

Retweeting• Purposive practices• Facilitates rapid information diffusion within

Twitter• Create collective minds among the users of

shared interests• Two types of retweeting: (1) Retweeting external content (2) Retweeting internally generated content : a

special case of second-level gatekeeping in Twitter

RQ2: What types of messages are re-circulated via retweeting among the internally generated content in Twitter?

Methods

• Topic: Israel-Gaza Conflict from Dec 27, 2008 to Jan 18, 2009 - one of the representative international conflict news - active use of social media: called ‘PR war”• Data: Tweets made by personal users

- 860 first-hand tweets (only external links) - 521 retweets (both internal content and external links)

• External sources cleaned including the top two or three level domain names (http://www.zzz.zzz or http://www.zzz.zzz.zzz )

• For temporal analysis: the data split into incident periods ▫ Early: Time 1 (Dec 27 – Jan 5)▫ Late: Time 2 (Jan 6 – Jan 18)

Variables• DV: a frequency of a content provider’s website

being selected

• IVs: (1) Content Types: Traditional media, Commercial

social media, Online journalism, Personal providers, Other org. /institutional/community websites (Cohen’s Kappa = .89)

(2) Hyperlinks: In-coming hyperlinks to a particular provider’s website as an indicator of its popularity (based on a global traffic)

Types Description Examples

Traditional Newswire, broadcasting, and print mass media. Offline presence Target mass audiences, wit a broad range of topics

cnn.com

aljazeera.com

guardian.co.uk

Social Media

Commercial company providing a space for users to easily create and share contents.

Sometimes, have an automatic aggregation function.

youtube.com

reddit.com

facebook.com

Online Journalism

The websites with journalistic writing style yet do not have offline edition.

Have independent domain names.

alternet.org

huffingtonpost

allvoices.com

Personal Informal websites run by either an individual or a small number of people.

Do not have formal organizational structure.

polizero.com

andycarvin.com

buzzsuggest.com

Others Any organizational/institutional/community websites that were not categorized in any of above.

E.g. governmental, corporate, educational, research, advocacy organizational websites.

gazatalk.com

un.org

arabmediasociety.com

Descriptive Results• A total of 256 unique content providers were

tweeted

• The average frequency of being tweeted is 3, yet with a huge variations, ranging from 41 times to 1 times.

• The most frequently tweeted:

Re-plotting after log-transformations (Test of Power-law distribution)

Highly Selective representation: Majority of providers (N = 176, 68.8%) selected only by a single user (R2 = .80, p < .0001) H1

supported

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.80

0.5

1

1.5

2

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log transformed k

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ransf

orm

ed P

(k)

H2~4: Negative Binomial Regression Models (NBR)

•Regression model with the count DV.

•Content Type is a categorical variable, requiring a reference level.

(1) ‘traditional media realm’ was hypothesized as to be more influential. (2) ‘commercial social media’ showed the highest mean frequency among all.

Descriptive AnalysisTIME 1 TIME2

FRQ HYP N FRQ HYP N

Traditional

2.92 9.34 48 2.64 9.27 55

Social media

3.43 10.74 28 5.75 9.30 22

On Journal

1.84 8.12 25 1.43 8.42 23

Personal 1.05 5.12 21 1.10 5.88 22

Other 1.88 6.66 33 2.47 8.21 35

Total 2.36 8.27 155 2.45 8.14 157

Hypotheses Testinglog(Tweet Frequency) = Intercept + b1(CT=traditional media)

+ b2(CT=online journalism) + b3(CT = personal) + b4(CT = org/inst/community) + b5Hyperlinks + b6

(CT=traditional media)*Hyperlinks + b7CT=(online journalism)*Hyperlinks + b8(CT=personal)*Hyperlinks + b9(CT=org/inst/community)*Hyperlinks.

• H2 (Hyperlinks effects): Supported on both stages

• H3 (Content Type effects): Not supported

• H4 (Interaction between Hyperlinks and Content Type): Only supported on the later stage.

Model Effects

TIME 1 TIME 2

Wald df Sig. Wald df Sig.

Intercept 2.13 1 0.144 0.01 1 0.959Hyperlinks 7.3** 1 0.007 5.09* 1 0.024CT 2.06 4 0.725 3.79 4 0.436CT x Hyperlinks 3.01 4 0.556 10.49* 4 0.033NOTE: CT = Content Types

Parameter Estimate for Time 2 (social media as a reference)

B SE C.I. Wald χ2

Exp (B)Low High

(Intercept)** -0.96 0.74 0.09 1.63 1.69 0.38

(CT =TM) 0.68 1.01 0.27 14.38 0.45 1.97(CT =OJ) 1.26 1.01 0.49 25.58 1.56 3.53(CT =Personal) 1.04 0.89 0.50 16.08 1.38 2.83(CT =Other) 1.74 0.94 0.90 36.15 3.41 5.70Hyperlinks *** 0.25 0.07 1.12 1.47 12.84 1.28

(CT =TM) x HP -0.12 0.10 0.73 1.08 1.39 0.89(CT = OJ)x HP* -0.24 0.10 0.64 0.96 5.60 0.79(CT = Personal) x HP *

-0.25 0.10 0.65 0.94 6.50 0.78

(CT = other) x HP**

-0.27 0.10 0.62 0.93 6.85 0.76

TIME 1 TIME 2

blue: traditional

green:social media

green:social media

blue: traditional

Qualitative Analysis of Retweeting internal contents (RQ2)

•45.1% were internally produced tweets.•News alerts from professional news

organization (BNO: 46 times, AlGaza: 38 times)

•134 include ordinary users’ emotional/expressive comments:

(1) tactical information (e.g. where and how to meet up for protest)

(2) expressive catchphrases (e.g. “War criminal Tony Blair called the situation Gaza ‘hell’”.)

Conclusion & Discussions (1)• Hyperlinks from general online public influences

users’ information selection process in Twitter.

• CT becomes influential as time goes by, but only when interacting with Hyperlinks effects: Traditional form of contents does not necessarily guarantee the successful ‘filtering-in’.

• The popular use of commercial social media among Twitter users: Smart adaptation of popular web applications (i.e. aggregator website) requited for less visible content providers (e.g. Human Rights campaign delivered by Youtube or CNN I-Report better than by its own website)

Conclusion & Discussions (2)• As a collective outcome, user selection reveals a few

prominent information providers and a large number of less visible providers…any implication regarding the diversification & decentralization of online news consumption?

• Retweeting to disseminate not only information but also emotions and strategic actions.

• Limitations… - no causal assessment possible - one specific case: question about generalizability? - English only

ReferencesBarzilai-Nahon, K. (2008). Toward a theory of network gatekeeping: A framework for

exploring information control. The Journal of the American Society of Information Science and Technology, 59(9), 1493-1512.

Katz, E. (1957). The two-step flow of communication: An up-to-date report on a hypothesis. The Public Opinion Quarterly, 21(1), 61-78

Meraz, S. (2009). Is there an elite hold? Traditional media to social media agenda setting influence in blog networks. Journal of Computer-Mediated Communication, 14, 682-707.

Napoli, P. M. (2008). Hyperlinking and the force of “massification.” In J. Turrow and L. Tsui (Eds.), The Hyperlinked Society (p.56-69). Ann Arbor, MI: The University of Michigan Press.

Hindman, M. (2008). The Myth of Digital Democracy. Princeton, NJ: Princeton University Press.

McQuail, D. (1994). Mass communication theory: An introduction, 3rd ed., London, UK: Sage.

Mitchelstein, E. & Boczkowski, P. J. (2010). Online news consumption research: An assessment of past work and an agenda for the future. New Media Society, 12, 1085-1102.

Sunstein, C. (2001). Republic.com. Princeton, NJ: Princeton University Press. Yim, J. (2003). Audience concentration in the media: Cross-media comparisons and

the introduction of uncertainty measure. Communication Monograph, 70(2), 114-128.

Thanks! Any Questions? Comments?