Using Social Media and Online Conversations to add Depth to Unemployment Statistics

Abstract

This research investigates whether and how social media and other online user-generated content could enrich understanding of the effect of changing employment conditions. The primary goal is to compare the qualitative information offered by social media with unemployment figures. To this end, we first selected online job-related conversations from blogs, forums and news from the United States and Ireland. For all documents, a quantitative mood score based on the tone of the conversations—for example, happiness, depression or anxiety—was assigned. The number of unemployment-related documents that also dealt with other topics such as housing and transportation was also quantified, in order to gain insight into populations’ coping mechanisms. This data was analyzed in two primary ways. First, the quantified mood scoring was correlated to the unemployment rate to discover leading indicators that forecast rises and falls in the unemployment rate. For example, the volume of conversations in Ireland categorized as showing a confused mood correlated with the unemployment rate with a lead-time of three (3) months. Second, the volume of documents related to coping mechanisms also showed a significant relationship with the unemployment rate, which may give insight into the reactions that can be expected from a population dealing with unemployment. For example, the conversations in the US around the loss of housing increased two (2) months after unemployment spikes. Overall, in this initial research, SAS and Global Pulse have underlined the potential of online conversations to complement official statistics, by providing a qualitative picture demonstrating how people are feeling and coping with respect to their employment status.

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