CSS Wintersymposium 2014 bei GESIS: Assistant Prof. Sophie Mützel (U. Luzern)
CSS Winter Symposium 2014 bei GESIS (http://www.gesis.org/veranstaltungen/...)
Assistant Prof. Sophie Mützel (U. Luzern):
Text as data: uniting structure and content in computational social science
Over the last couple of years and from a range of theoretical perspectives, social science research has repeatedly pointed to new challenges and new opportunities for data collection, data analysis and sociological explanations (e.g. Latour et al. 2012; Lazer et al. 2009; Savage/Burrows 2007): given our ubiquitous use of digital media, we generate heaps of data that allow insights into, for instance, consumer and social behavior at large scale. Increasingly, social research does not use survey or interview data anymore, which relies on ex post or a priori evaluation, but where and when possible analyzes large amounts of relational data that is generated while social processes are taking, e.g. when communicating or consuming (e.g. Kossinets/Watts 2006; Pentland 2014).
These types of relational data are already available in digital form. However, there are also gigantic amounts of already existing textual data, which relate to each other and/or refer to the same things or actors: e.g. printed media reports, speeches, minutes of meetings, press statements, evaluative reviews, letters, contracts. My talk argues for the systematic use and analysis of texts as data using large-scale data sets. Based on different empirical studies, I show how large amounts of media reports can be first digitized and cleaned to then be formally analyzed using computational tools such as topic modeling. Originally developed in computer science, machine learning, and computational linguistics, the method of topic modeling sorts together terms that cooccur into semantic contexts of relational meaning and thus identifies the themes that structure a textual corpus. The themes can be understood as frames (Gamson 1992). It is thus a method that captures many elements of a cultural sociology interested in the measurement of meaning and taking relationality into account (e.g. Mohr 1998; Mohr/Bogdanov 2013). In sum, my presentation bridges between current discussions in digital humanities, where huge amounts of text and image data sources are digitized and related to each other, and in computational social science, which uses digitally generated data and algorithmic tools to explain social action – using a sociological perspective which searches for patterns in order to find explanations.
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