Thick-Big Descriptions

TY - ABST

T1 - Thick-Big Descriptions

AU - Lai, Signe Sophus

PY - 2017/9/29

Y1 - 2017/9/29

N2 - The paper discusses the rewards and challenges of employing commercial audience measurements data – gathered by media industries for profitmaking purposes – in ethnographic research on the Internet in everyday life. It questions claims to the objectivity of big data (Anderson 2008), the assumption that bigger is always better, and the many legacy decisions and rules that ultimately govern how audiences are ‘made’ in commercial measurement companies. As such, the paper extends the discussions of a previous empirical study (Lai 2016) on how media organizations imagine their audiences (Ang 1991; Napoli 2010; Webster 2014). This study evolved around industry stakeholders resisting and negotiating changes, as they are happening, in media consumption dynamics and measurement standards, which inevitably reconceptualize future institutionally effective audiences (Ettema & Whitney 1994). With digital communication systems, language and behavior appear as texts, outputs, and discourses (data to be ‘found’) – big data then documents things that in earlier research required interviews and observations (data to be ‘made’) (Jensen 2014). However, web-measurement enterprises build audiences according to a commercial logic (boyd & Crawford 2011) and is as such directed by motives that call for specific types of sellable user data and specific segmentation strategies. In combining big data and ‘thick descriptions’ (Geertz 1973) scholars need to question how ethnographic fieldwork might map the ‘data not seen’ (Baym 2013) in big data, and how web-measurement practices expose significant cultural and political aspects of the contexts they operate in.

AB - The paper discusses the rewards and challenges of employing commercial audience measurements data – gathered by media industries for profitmaking purposes – in ethnographic research on the Internet in everyday life. It questions claims to the objectivity of big data (Anderson 2008), the assumption that bigger is always better, and the many legacy decisions and rules that ultimately govern how audiences are ‘made’ in commercial measurement companies. As such, the paper extends the discussions of a previous empirical study (Lai 2016) on how media organizations imagine their audiences (Ang 1991; Napoli 2010; Webster 2014). This study evolved around industry stakeholders resisting and negotiating changes, as they are happening, in media consumption dynamics and measurement standards, which inevitably reconceptualize future institutionally effective audiences (Ettema & Whitney 1994). With digital communication systems, language and behavior appear as texts, outputs, and discourses (data to be ‘found’) – big data then documents things that in earlier research required interviews and observations (data to be ‘made’) (Jensen 2014). However, web-measurement enterprises build audiences according to a commercial logic (boyd & Crawford 2011) and is as such directed by motives that call for specific types of sellable user data and specific segmentation strategies. In combining big data and ‘thick descriptions’ (Geertz 1973) scholars need to question how ethnographic fieldwork might map the ‘data not seen’ (Baym 2013) in big data, and how web-measurement practices expose significant cultural and political aspects of the contexts they operate in.

KW - Det Humanistiske Fakultet

KW - audience studies

KW - audiences

KW - audience measurement

KW - big data

KW - Ethnographic method

KW - ethnography

M3 - Konferenceabstrakt til konference

T2 - Audiences 2030

Y2 - 28 September 2017 through 29 September 2017

ER -