This workshop will bring together researchers working on automated content analysis for large scale data and social scientists interested in tracking changes in public opinion.

Online communication and digital technologies allow drawing real-time indicators of what the public think. These indicators offer great research potential because they rely not on a preconception of the issues that are salient in the mind of the public (as surveys usually do) but on what the public considers important enough to voluntarily talk about it. However, the research potential of online communication is still largely underexploited in the social sciences because of many unsolved measurement and validity issues: How representative is online communication of general public opinion? How reliable are online indicators when measured using different sources and methods? How comparable are they to long-standing measures drawn from polls and surveys? And how much explanatory power do they have, on the aggregate, to explain offline political behaviour?

This workshop will bring together researchers working on automated content analysis for large scale data and social scientists interested in tracking changes in public opinion. The aim is to review different methods to parse online communication and extract indicators of what the public think (i.e. salient issues) and how they think about it (i.e. the framing of those issues). The discussion will focus on three aspects: (1) How available tools (open-source and proprietary) compare to each other when it comes to extracting opinions from text; (2) The compatibility of those tools with other modeling approaches and aims (semantic networks and forecasting); and (3) the application of automated content analysis to substantive domains of public opinion research, paying especial attention to how online communication can improve (or not) more traditional measurements based on polls and surveys

This workshop is funded by the OUP Fell Fund.