Abstract
Sentiment and topic analysis are common methods used for social media monitoring. Essentially, these methods answers questions such as, “what is being talked about, regarding X”, and “what do people feel, regarding X”. In this paper, we investigate another venue for social media monitoring, namely issue ownership and agenda setting, which are concepts from political science that have been used to explain voter choice and electoral outcomes. We argue that issue alignment and agenda setting can be seen as a kind of semantic source similarity of the kind “how similar is source A to issue owner P, when talking about issue X”, and as such can be measured using word/document embedding techniques. We present work in progress towards measuring that kind of conditioned similarity, and introduce a new notion of similarity for predictive embeddings. We then test this method by measuring the similarity between politically aligned media and political parties, conditioned on bloc-specific issues.
Highlights
Social Media Monitoring (SMM; i.e. monitoring of online discussions in social media) has become an established application domain with a large body of scientific literature, and considerable commercial interest
To answer the language similarity question posed by issue ownership we measure aggregate predictive similarity between party platforms and various subsets of online text data, conditioned on words pertaining to left wing issues, right wing issues, nativist issues, and general political topics
In collaboration with the Political Science department at Gothenburg University we extracted keywords for each party from their party platform. We use these party specific keywords as a crude proxy for issues: we let left wing issues be defined by the union of left bloc party keywords, right wing issues be defined by right bloc party keywords, and nativist issues be defined by the
Summary
Social Media Monitoring (SMM; i.e. monitoring of online discussions in social media) has become an established application domain with a large body of scientific literature, and considerable commercial interest. The central questions SMM seeks to answer are “what do users talk about?” and “how do they feel about it?” Answers to these questions may provide useful insight for market research and communications departments. It is apparent how product and service companies may use such analysis to gain an understanding of their target audience. To measure that kind of conditioned similarity we introduce a new notion of similarity for predictive word embeddings This method enables us to manipulate the similarity measure by weighting the set of entities we account for in the predictive scoring function. For example, observe that while the Left Party representation is, overall, similar to that of nativist media, it differs significantly on nativist issue, while this effect is not seen to the same extent on more mainstream left wing or right wing media
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