Umati: Kenyan Online Discourse to Catalyze and Counter Violence

IFIP Working Group 9.4

12th International Conference on

Social Implications of Computers in Developing Countries

Conference Theme:

Into the Future: Themes Insights and Agendas for

ICT4D Research and Practice

Sunset Jamaica Grande, Ocho Rios, Jamaica

May 19 - 22, 2013

THE UNIVERSITY

OF THE WEST INDIES

MONA

Conference Proceedings

TABLE OF CONTENTS

WELCOME – CONFERENCE CHAIRS 2

WELCOME – ORGANIZING CHAIRS 3

CONFERENCE COMMITTEE 4

TRACK CHAIRS 5

PROGRAMME COMMITTEE 6

SPONSORS 7

EXHIBITORS 10

KEYNOTE SPEAKERS 16

PANELS 20

TRACK INTO THE FUTURE 25

TRACK UNIVERSITY-COMMUNITY ENGAGEMENT 334

TRACK SEN’S CAPABILITY APPROACH AND ICT4D 394

TRACK SOCIAL MEDIA AND DEVELOPMENT 467

TRACK UNDERSTANDING THE ACTORS: ACTOR-NETWORK THEORY IN ICT FOR DEVELOPMENT RESEARCH

504

TRACK HOW ICT FRAME DEVELOPMENT GOALS 568

TRACK ICTS, COLLABORATION AND SERVICE INNOVATION: BRIDGING BOUNDARIES AND CULTURES

682

TRACK ICTD IN THE CARIBBEAN - ARTICULATING UNIQUE CHALLENGES AND SOLUTIONS

766

TRACK CARING FOR A CONNECTED HUMANITY: EHEALTH, AND THE TRANSFORMATION OF HEALTHCARE IN THE GLOBAL SOUTH

826

TRACK DESIGNING APPLICATIONS, SERVICES, SYSTEMS AND INFRASTRUCTURE FOR DEVELOPMENT

903

PHD GRADUATE STUDENT TRACK 950

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UMATI: KENYAN ONLINE DISCOURSE TO CATALYZE AND COUNTER VIOLENCE

Kagonya Awori, iHub Research, Kenya

Susan Benesch, American University, U.S.A

Angela Crandall, iHub Research, Kenya

Email: [email protected], [email protected], [email protected]

Abstract: Email and SMS were heavily used in Kenya to spread inflammatory speech, rumors

and threats during the months before the 2007 presidential election and subsequent mass violence. It is widely believed that online discourse helped catalyze the violence, but this remains a hypothesis. Building on research in genocide studies, speech act theory and discourse theory, Susan Benesch has proposed a system of discourse analysis to identify “dangerous speech”, which is discourse that may catalyze violence by one group against members of another. To test this theory and to build the first database of inflammatory speech in a country’s online space, iHub Research and Ushahidi captured, and performed analysis of, Kenyan inflammatory discourse online in seven separate languages since September 2012. In its first six months, this ‘Umati” (“crowd” in Kiswahili) monitoring project yielded more inflammatory speech than expected, some of it explicit and violent, especially in the weeks surrounding the March 2013 presidential election (Kenya’s first since 2007). We responded by designing a small experiment to diminish inflammatory speech online. We also captured a strikingly large body of social media discourse calling for peace and calm, in the immediate aftermath of the 2013 election, which was almost entirely free of violence. In this report on our work in progress, we describe our findings thus far and pose new questions for research, including further study of our data.

Keywords: Dangerous Speech; Election Monitoring; Kenya; Social Media; iHub; Ushahidi

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Awori et al. Umati: Kenyan Online Discourse to Catalyze and Counter Violence

UMATI: KENYAN ONLINE DISCOURSE TO CATALYZE AND COUNTER VIOLENCE

1. INTRODUCTION On March 4, 2013, Kenya held its first general election since the 2007 polls when disputed results triggered a deadly crisis. In 2007/8, more than 1,300 people were killed and an estimated 663,921 displaced in inter-tribal attacks (Associated Press, 2011). Media technologies such as mobile phone Short Message Service (SMS) and community and vernacular radio were used so widely to advocate hatred and violence, and apparently to mobilize communities to action (Osborn, 2008) that some observers suggested that the mobile phone had become a ‘weapon of war’ (Bangre’, 2008). Anecdotal evidence suggests that social media also played a role in mobilizing Kenyans, who have been adopting ICT (Information and Communication Technology) and social media platforms very rapidly, even at the bottom of the socioeconomic scale (Mäkinen & Kuira, 2008; Goldstein, 2008; infoDev, 2012).

However documentation of inflammatory speech online is lacking, due to scant monitoring in 2007/8. This has made it difficult to study. Systematic study is also hindered by the lack of a definition of inflammatory speech or “hate speech.” Since the goal of our work is to prevent violence while also protecting freedom of expression, we chose to define our dataset more narrowly than “hate speech.” We use an analytical framework designed to describe discourse that has a reasonable possibility of catalyzing violence, in the context in which it was made or disseminated (Benesch, 2008, 2013).

If it is true that discourse transmitted via ICT platforms helps to catalyze violence in the Kenyan context and in other countries1, we wish to find ways of diminishing that effect (again, without impinging on freedom of expression - or privacy). The unique network-building capacities of social media may be well suited to this effort, and experiments are already underway in several countries, including Kenya (PeaceTXT, 2012).

In the weeks leading up to the 2013 vote, we began an online peace-keeping effort of our own called Nipe Ukweli (“Give me Truth” in Kiswahili), designed to counter dangerous speech and especially malicious rumors, which were a common and destructive form of discourse during the clashes of 2007/8 (Osborn, 2008). In fact, during the 2013 election and the five tense days following it, while votes were being counted at a frustratingly slow rate, many Kenyans posted and Tweeted appeals for peace, calm, patience, and national unity. Since Uhuru Kenyatta was declared president-elect on March 9, we have unfortunately documented a dramatic spike in inflammatory speech. On the Monday following the announcement of the new President-elect alone, the Umati project collected 61 examples of Dangerous Speech, the highest daily count noted in over three months (The Umati Project, 2013).

2. RESEARCH QUESTIONS This project aims to help fill a gap in the literature pointed out by Garrett (2006) on the negative consequences of new ICTs, by testing a methodology to systematically track and classify levels of inflammatory speech online. Our first questions therefore address the effectiveness of the methodology for classification, itself:

1 The use of SMS and social media to incite violence and fear of violence has been documented in many countries such as India and Australia. See example, Yardley, 2012.

Proceedings of the 12th International Conference on Social Implications of Computers in Developing Countries, Ocho Rios, Jamaica, May 2013

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• Does the Benesch framework for discourse analysis provide consistent classification among monitors and among languages? Do categorization decisions made using the analysis framework match monitors’ subjective estimations of the dangerousness of a particular act of speech?

Our project’s purpose (and Benesch’s goal in designing the framework) is to probe the relationship between inflammatory speech (especially online discourse) and violence. The following questions inspired this project, therefore, although further research will be required to answer them:

• What are the effects of inflammatory speech online? Is there a causal link between inflammatory speech online and violence offline?

Online public speech provides a special opportunity for data collection to answer these questions, since reaction to speech online can be tracked and quantified more effectively than reaction to offline speech.

We suggest further study using three distinct methodologies: 1) using network analysis to investigate the impact that particular acts of speech have on audiences online; 2) comparing data on inflammatory speech acts and data on acts of violence, for correlations in geo-location and timing, building upon the unique work of Yanagizawa-Drott (2012); 3) qualitative fieldwork such as Osborn (2008)’s study of rumor in the informal Nairobi settlement of Kibera in 2007/8. Osborn concluded that rumors not only raised apprehension and fear, but also incited to action. Like Yanagizawa-Drott, Osborn not only posits a link between inflammatory speech and collective violence, but supports it with evidence2. Thus far, this is exceedingly rare in the literature.

3. DISCOURSE ANALYSIS: DANGEROUS SPEECH VERSUS HATE SPEECH Benesch’s methodology for discourse analysis of inflammatory speech (2008, 2013) is designed to identify hate speech that has a special capacity to inspire violence because of the construction and reconstruction of narrative that it helps to drive. Building on the work of social psychologists, historical sociologists and genocide scholars such as Ervin Staub (1989, 2003), Helen Fein (1979), Frank Chalk and Kurt Jonassohn (1990), Philip Zimbardo (2007), and James Waller (2007), and drawing on speech act theory (Austin, 1962; Searle, 1975), as well as discourse analysis of many historical cases of inflammatory speech that preceded episodes of mass violence, Benesch has built an analytical framework designed to provide a qualitative but systematic evaluation of the capacity of a particular act of speech to inspire an audience to violence against members of another group. Since the “force,” or capacity of a speech act to inspire action, is context-dependent, the evaluation must take into account the context in which the speech was made or disseminated, and must be conducted by an analyst with knowledge of that cultural, social, and historical context.

“Hate speech” is a very broad category, defined in disparate ways in law and in common parlance, but generally understood to mean speech that denigrates people on the basis of their membership in a group, such as an ethnic or religious group. It includes speech that does not

2 To our knowledge, only one scholar has produced quantitative evidence of a link between inflammatory speech and mass violence: David Yanagizawa-Drott (2012) found higher levels of killing in the 1994 Rwandan genocide in villages that received the radio signal of the notorious station Radio Television Libre des Milles Colllines (RTLM), then in villages where the signal did not reach. For a contrasting view, see Straus (2007).


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appreciably increase the risk of violence, although it may well cause emotional and psychological damage and increase tensions between groups.

Dangerous Speech is a narrower category, first coined and defined by Benesch (2013). It is speech or another form of expression that has a reasonable chance of catalyzing or amplifying violence by one group against another. We were especially interested in Dangerous Speech as we began our project only months away from the date of Kenya’s first presidential and full parliamentary elections since the post-election violence of 2007-8. Renewed violence was widely feared.

Our coding sheet was based on Benesch’s analytical framework for identifying Dangerous Speech. The framework, in turn, is built around five criteria that affect the dangerousness of a particular speech act in the time and place in which it was made or disseminated: the speaker, the audience, the speech act itself, the social and historical context, and the mode of dissemination of the text.

To illustrate, the idea-type of the most dangerous speech act would be one for which all five variables are maximized:

• a powerful speaker with a high degree of influence over the audience;

• an audience with grievances, fear and other vulnerabilities that the speaker can cultivate, and that make the audience especially susceptible to incitement;

• a speech act which, although it may be coded or elliptical on its face, is clearly understood as a call to violence by the audience most likely to react;

• a social or historical context that is propitious for violence, for any of a variety of reasons, including long-standing competition between groups for resources, lack of efforts to solve grievances, or previous episodes of violence;

• a means of dissemination that is influential in itself, for example because it is the sole or primary source of news for the relevant audience.

The criteria are not ranked, nor are they weighted equally across cases: in many circumstances, one or more variables will ‘weigh’ more than others. For example, an especially outrageous or frightening speech act may be more important (i.e., more dangerous) than other factors in a particular instance. In other cases, an audience may be especially susceptible. It is also possible for an act of speech to cross the “dangerous” threshold based on only two, three, or four of the five criteria.

In our coding sheet, we emphasized the first three criteria (i.e., an influential speaker, a susceptible audience and inflammatory content of the text) rather than the latter two for the following reasons: we found it difficult to determine temporal boundaries for the historical context of each piece of text collected in the project, and the means of dissemination for all discourse collected was always similar, i.e. the blogosphere and social media sites.

4. MONITORING AND CATEGORIZATION PROCESS Over a period of at least 8 months, beginning September 2012 and continuing until April 2013 or later, the Umati project is monitoring Kenyan online discourse in order to estimate the occurrence and virulence of hate and dangerous speech. We employ teams of six human monitors, working in the most prevalent languages online in Kenya: the vernacular languages of the four largest ethnic groups in Kenya (Kikuyu, Luhya, Kalenjin and Luo), Kenya’s national language Swahili and the unofficial slang language, Sheng, which is used widely in urban


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centers, and Somali, spoken by the largest immigrant community in Kenya (Kenya National Bureau of Statistics, 2010; Githiora, 2002).

Each monitor is required to scan online platforms for incidences of hate speech and save them into an online database through the use of a coding sheet. Apart from providing discourse analysis about each statement, answers to these questions allow for additional qualitative research to be conducted in other areas including crowd sourcing, machine learning, human monitoring, ethnic diversity, influence of religion on speech, and translation of vernacular languages.

All texts are translated into English, and then sorted into three categories: offensive speech, moderately dangerous speech, and extremely dangerous speech. For this, the monitors consider two questions:

i) On a scale of 1 to 3 with 1 being little influence and 3 being a lot of influence, how much influence does the speaker have on the audience? (code = N)

ii) On a scale of 1 to 3 with 1 being barely inflammatory and 3 being extremely inflammatory, how inflammatory is the content of the text? (code = M)

The answers given to these two questions are dependent on the perceivable influence the speaker has on the online audience most likely to react with violence, the content of the statement, and the social and historical context of the speech statement. The sorting formula is this:

SORTING

M1 + N1 = Bucket 1

M1 + N2 = Bucket 1

M1 + N3 = Bucket 2

M2 + N1 = Bucket 2

M2 + N2 = Bucket 2

M2 + N3 = Bucket 3

M3 + N1 = Bucket 3

M3 + N2 = Bucket 3

M3 + N3 = Bucket 3

BUCKETS

Bucket 1 = Offensive Speech

Bucket 2 = Moderate dangerous speech

Bucket 3 = Extremely dangerous speech

Categorization of the hate speech statements in these three buckets facilitates more comprehensive qualitative and quantitative research. Some of the findings from the data thus far are:

a) Influential speakers have a significant impact on discussions online. For example, in September 2012 when Ferdinand Waititu, a member of parliament from Nairobi, gave a public speech calling for the expulsion of Maasai people from the city (Jambo News, 2012), the


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impact online was significant. A Twitter protest was started calling for Waititu’s arrest, but within three days of his statements, 45 incidences of moderate to extremely dangerous speech were recorded either against Waititu or in line with his incitement against the Maasai.

b) Events reported by mainstream media have a direct impact on online conversations; in the example above, the circulation of a video clip (NTV Kenya, 2012) of Waititu’s speech resulted in a rise in hateful comments online about that ethnic group.

5. WAY FORWARD; NEXT STEPS IN RESEARCH The Umati project will continue past the March 2013 elections. We believe our data provides fruitful avenues for further work; both the data and the methodology will be made available to scholars and practitioners. While welcoming ideas, we suggest the following next steps:

5.1 Automation We hope to find ways to automate aspects of the monitoring process. It will not be sufficient simply to search for key words, since words and phrases can be highly offensive or innocuous, depending on context. Perhaps it would be possible to employ machine learning to search for attributes of dangerous speech that humans are unable to detect. We wish also to compile dangerous speech datasets in the online spaces of other countries, in order to compare attributes of dangerous speech across environments.

5.2 Countering Inflammatory Speech We are also working to develop and test non-government methods for countering dangerous speech online, as we have done with our nascent project Nipe Ukweli, which encourages online actors to resist inflammatory speech, especially rumors, and to refute rumors with evidence of their falsehood. We seek to better understand online audiences and the effects of diverse platforms on online discourse norms. We are interested in comparing techniques across platforms. For example, PeaceTXT (2012) and Sisi ni Amani (sisiniamani.org) distribute peace messages to mobile phone users via SMS. What effect might result when such messages arrive instead as Tweets? Similarly, research should be conducted to understand whether some inflammatory discourse that was previously disseminated on SMS has now migrated to social media in Kenya. This may present new opportunities for working to shift the norms of discourse in online spaces.

6. ACKNOWLEDGEMENTS The Umati Project would like to thank our generous donors – PACT, Chemonics International - Kenya Transition Initiative, the MacArthur Foundation, and our partner Ushahidi for making this work possible.

7. REFERENCES AND CITATIONS Associated Press. (2011, September 1). Kenya violence suspects face ICC hearing. Al Jazeera

English. Retrieved from http://www.aljazeera.com/video/africa/2011/09/201191171934823573.html


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Yardley, J. (2012, August 18) Panic Seizes India as a Region’s Strife Radiates. The New York Times. Retrieved from http://www.nytimes.com/2012/08/18/world/asia/panic-radiates-from-indian-state-of-assam.html?ref=world&_r=0.

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Umati: Kenyan Online Discourse to Catalyze and Counter Violence

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