Session 3: Meaning and interpretation: Is big data any different than small data?

(Reflections from Stefanie Habersang)

In the afternoon Joseph Porac draws from his large expertise in socio-cognitive dynamics as well as computerized text analysis and gave a highly interesting talk about “Meaning and interpretation: Is Big (Text) Data any Different than Small data?” In his talk he presents some very convincing examples what we can learn from the interpretation of small data to better interpret and translate big data. Drawing on two extreme translation concepts (Derrida’s non-referential deconstruction, in which translating any text into stable meaning is almost impossible vs. google translation, in which universal translations are easily generated in a “quick-and-dirty” manner or “good-enough” fashion) Joseph Porac exemplifies the difficulties that we face when we talk about meaning-making and translation. He points out that basically everything is always about meaning-making, e.g. how we interpret the results of an experiment, how we interpret a stylized questionnaire to develop meaningful questions for a certain research setting, and how we attach (or cannot attach) meaning to things that we have not experienced on our own.

He then draws on Peter Winch’s 1958 book “The Idea of a Social Science and its relation to Philosophy” and introduces three theses to illustrate that the issues of meaning and interpretation are deeply embedded in the idea of social science – independent of big or small data. The first thesis he discusses is the “the rule thesis”. This thesis postulates that understanding human language use involves seeing the rules or properties in accordance with which it is produced, not just regularities in its production. If we wish to understand how a person represents things, in particular what they say about things, then we need to know the rules that govern their thoughts and words. We need to know what would make it right for them to say what they say, and what would make it wrong. In this sense, the context from which these rules emerge is fundamental for understanding – and this line of thinking is equally applicable when we want to interpret big data. Second, “the practicability thesis” states that understanding human language use does not mean just grasping the intellectual ideas that permeate it but, more deeply, cottoning to the practical orientations of the actors. While human language and action essentially involve rule-following, the rules in question cannot all be grasped in an intellectual manner. The main take-away here is that rule-following ultimately rests on a foundation of practice. Finally, the third thesis is the “the participation thesis”. It states that understanding human language use involves participating in the society of the agents, at least in imagination, not just standing back and surveying that which they are doing. This is closely related to the aspect that interpretation involves empathy for the ones that we research. As such, it does not matter if we interpret small or big data we as researchers cannot and should not act as detached observers.

The important take-away from this talk was that the above mentioned issues of meaning and interpretation will not “go away” with the rise of big data. On the contrary, with big data these issues may be exacerbated. And although big data becomes increasingly important in social science, small data and the insights that we can draw from it will not be going away anytime soon. Hence, Joseph Porac closed his talk by emphasizing the necessity of an interpretive data science.