Data augmented ethnography: 
using big data and ethnography to explore candidates' digital interactions

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In this paper we propose data augmented ethnography as a novel mixed methods approach to combine ethnographic, qualitative, observations with social media data collection and computational analysis. Using two brief studies on online interaction as examples we discuss the benefits and challenges of the combination of these two perspectives. We posit that the observations made in the qualitative phase can be quantified and hypothesized together with the data collected later during the analysis stage. Through our case studies we aim to shed light to the differences apparent on the party level and seek to understand how candidates, based on their parties political standing, differ in terms of interactivity. We ask, what insights does a mixed-method approach combining ethnographic observations to computational social science offer to the study of interactivity and its many pregnant forms? To answer this question, we use a large data set collected from different social media platforms before and during the 2015 Parliament Election in Finland. This data consists of both textual data including all candidate updates and the conversations they elicited, as well as field notes written and collected during ethnographic field work period before the elections.

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Data augmented ethnography: 
using big data and ethnography to explore candidates' digital interactions

  1. 1. Data augmented ethnography: 
 using big data and ethnography 
 to explore candidates' digital interactions Salla-Maaria Laaksonen, Matti Nelimarkka, 
 Mari Tuokko, Mari Marttila, Arto Kekkonen, Mikko Villi #digivaalit2015 @jahapaula
  2. 2. Context Finnish Parliamentary Elections 2015 • Computational data collection from Twitter, Facebook and 19 national news media outlets • Online ethnography (netnography / media ethnography) field work for 1 month before the Election date Original research focus • How candidates and other actors influence media agenda through social media? Extended focuses • Interaction in social media • Hashtags in election campaigns • Interest groups during the election campaigning
  3. 3. Computational social science Practices of utilizing computational, algorithmic methods to study society and social questions. (e.g., Lazer et al., 2009) • data collection • data preprocessing • data analysis
  4. 4. Burning questions 
 for social science and big data ! Do you know the context where data was collected? (e.g. boyd & Crawford, 2012, ; Gillespie 2014) ! Are you sure your algorithms compute the right thing? (e.g. Grimmer & Stewart, 2013) ! What about data selection and cleaning of your data? (e.g. Ekbia et al., 2015)
  5. 5. • Aims to create understanding and make sense of human life and social communities. • Usually conducted in the natural environments of human action. • Field work • Participatory observation • Documents Ethnography Picture: Malinowski/ WikimediaCommons
  6. 6. • Several different sub- approaches: webnography, network ethnography, netnography, media ethnography, trace ethnography… • Challenges and questions (e.g. Wittel, 2000; Markham, 2013): • What counts as participatory observation online? • Displacement between ethnographer and the field, lack of physical context • Relationship between the online and “the real”? Online Ethnography Picture: DaveDen / Uncyclopedia
  7. 7. Illustrative example studies: Candidates & interaction in Facebook Common theme for political research (e.g. Stromer-Galley 2000, 2004; Grahamn et al. 2013; Goldbeck et al. 2010, Larsson 2015, Grant et al. 2010) RQ1: What kind of differences we observe between the parties on interactivity in social media services? RQ2: To what degree we observe negative campaigning in candidate-candidate interaction?
  8. 8. Data and Method Ethnographic field work • Observation conducted by three researchers 19.3. – 19.4.2015 • Facebook and Twitter researcher accounts created, Tweetdeck used for Twitter • Focus on the forming of the online agenda around the election, candidate communication styles and interaction with other actors Social media data sets • All public Facebook pages of the candidates (n = 1111) • Collected using a social media monitoring tool 99analytics and FQL language • In total, 61 790 updates and 67 956 comments to those updates. • Extracting the authors of posts and comparison with Kruskal-Wallis Χ2-test and sentiment analysis using SentiStrenght (Thelwall et al. 2010)
  9. 9. RQ1: Party differences Small parties interact the most: Quantified interaction levels between large parties, small parties and non- parliamentary parties are significantly different (p≈0.02).
  10. 10. RQ2: Candidate interaction Engaging other candidates from other parties with harsh conversation styles (both Twitter and FB). The world's highest tax rate is not an competitive advantage. To which country are your referring here @Calle_Haglund? I would rather concentrate on questions considering Finland 
 (tweet from the chairman of the Left Alliance, April 7th 2015) @paavoarhinmaki By saying this I am trying to tell where Finland would end up to if for example the Left Alliance got to realize their election promises 
 (tweet from the chairman of the Swedish People's Party, April 7th 2015) Sentiment analysis: comments made to other party candidates’ FB pages are more negative in tone (p≈0.002)
  11. 11. Burning questions 
 for social science and big data ! Do you know the context where data was collected? (e.g. boyd & Crawford, 2012) ! Are you sure your algorithms compute the right thing? (e.g. Grimmer & Stewart, 2013) ! What about data selection and cleaning of your data? (e.g. Ekbia et al., 2015) Ethnography enhances contextual framing Human observations supports the interpretation (Methodological triangulation) Ethnography 
 supports data collection + big data sets allow generalization of the results + computational methods allow bigger data sets
  12. 12. Discussion… Data augmented ethnography
  13. 13. Discussion… 1. Data and observations always remain incomplete: the data that is visible for an observing researcher but also data sets, collected handles or hashtags,; the intention and nuances of communication 2. Researchers need to be ready to follow phenomena as they unfold: but elections have a schedule, riots do not 3. Making some selections needed during the analysis process
  14. 14. #vaalit2015 Thank You! ! Project home page: 
 https://www.hiit.fi/digivaalit-2015 Rajapinta blog: http://www.rajapinta.co We thank Helsingin Sanomat Foundation for providing funding through the project “Digivaalit 2015”. We also thank Kone Foundation for providing funding through the project “Digital Humanities of Public Policy-making”.

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