Measuring Issue Ownership using Word Embeddings
This work addresses the need for social media monitoring tools in political science to analyze issue alignment, though it appears incremental as it builds on existing embedding techniques.
The paper tackles the problem of measuring issue ownership and agenda setting in social media by proposing a method based on word/document embeddings to assess semantic similarity between sources and political parties on specific issues, and tests it by analyzing politically aligned media and parties on bloc-specific issues.
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.