Chase Hemphill references *back* in time

[divilian]

Add to Zotero whatever looks worth adding.

[vgcagle]

Conover- Political Polarization on Twitter create a corpus of political communication- a tweet containing at least one politically relevant hashtag cluster analysis of retweet and mention networks manually annotated users political identity retweet and mention networks homogeneous vs heterogeneous content

[vgcagle]

Laver- Extracting Policy Positions from Political Texts Using Words as Data reference texts with known positions, generate word scores from reference texts, score new text using the word scores

[vgcagle]

Lowe- Scaling Policy Preferences from Coded Political Texts propose an alternate approach to estimating policy positions based on the logarithm of odds ratios

[vgcagle]

Slapin & Proksch -A Scaling Model for Estimating Time-Series Party Positions from Texts scaling algorithm called WORDFISH- estimate policy positions based on word frequencies in text

[vgcagle]

Monroe- Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict Dirichlet prior Laplace prior p.396-398 political polarization

[vgcagle]

Lowe- Understanding Wordscores Given R documents or ‘‘reference texts’’ with known positions or scores on a policy dimension, Wordscores attempts to estimate the scores of L out-of-sample documents, the ‘‘virgin texts.’’ To do so the method first estimates scores for each word type occurring in the reference texts and then combines these wordscores into a score for each virgin document. It is important to distinguish the two parts: estimating wordscores and estimating document scores using wordscores because they are, at least in principle, independent parts of the method. There is usually a third and final part of the method that rescales virgin document score estimates, so they can be more easily compared with the reference text scores.