1. (in a long line of LiWC-like lexicons) Chenhao Tan's list of hedging phrases, such as "I suspect", "raising the possibility": README ;  list itself  

   References and Applications

   Chenhao Tan and Lillian Lee, "Talk it up or play it down? (Un)expected correlations between (de-)emphasis and recurrence of discussion points in consequential U.S. economic policy meetings", Text As Data 2016

   Abstract: In meetings where important decisions get made, what items receive more attention may influence the outcome. We examine how different types of rhetorical (de-)emphasis — including hedges, superlatives, and contrastive conjunctions — correlate with what gets revisited later, controlling for item frequency and speaker. Our data consists of transcripts of recurring meetings of the Federal Reserve’s Open Market Committee (FOMC), where important aspects of U.S. monetary policy are decided on. Surprisingly, we find that words appearing in the context of hedging, which is usually considered a way to express uncertainty, are more likely to be repeated in subsequent meetings, while strong emphasis indicated by superlatives has a slightly negative effect on word recurrence in subsequent meetings. We also observe interesting patterns in how these effects vary depending on social factors such as status and gender of the speaker. For instance, the positive effects of hedging are more pronounced for female speakers than for male speakers.

    

   Chenhao Tan,  Vlad Niculae, Cristian Danescu-Niculescu-Mizil, Lillian Lee. "Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions." Proc. of WWW 2016

   Abstract: Changing someone's opinion is arguably one of the most important challenges of social interaction. The underlying process proves difficult to study: it is hard to know how someone's opinions are formed and whether and how someone's views shift. Fortunately, ChangeMyView, an active community on Reddit, provides a platform where users present their own opinions and reasoning, invite others to contest them, and acknowledge when the ensuing discussions change their original views. In this work, we study these interactions to understand the mechanisms behind persuasion.

   We find that persuasive arguments are characterized by interesting patterns of interaction dynamics, such as participant entry-order and degree of back-and-forth exchange. Furthermore, by comparing similar counterarguments to the same opinion, we show that language factors play an essential role. In particular, the interplay between the language of the opinion holder and that of the counterargument provides highly predictive cues of persuasiveness. Finally, since even in this favorable setting people may not be persuaded, we investigate the problem of determining whether someone's opinion is susceptible to being changed at all. For this more difficult task, we show that stylistic choices in how the opinion is expressed carry predictive power.

2. language models, which assign probabilities P(x) to words, sentences or text units.
   These are great for similarity, distinctiveness, visualization
   

   1. Monroe et al's "Fightin words": what makes two "languages" different?
      Slides

      References and links

      Monroe, Burt L., Michael P. Colaresi, and Kevin M. Quinn. 2008. Fightin' words: Lexical feature selection and evaluation for identifying the content of political conflict.  Political Analysis 16(4): 372-403. [alternate link]

      Abstract: Entries in the burgeoning “text-as-data” movement are often accompanied by lists or visualizations of how word (or other lexical feature) usage differs across some pair or set of documents. These are intended either to establish some target semantic concept (like the content of partisan frames) to estimate word-specific measures that feed forward into another analysis (like locating parties in ideological space) or both. We discuss a variety of techniques for selecting words that capture partisan, or other, differences in political speech and for evaluating the relative importance of those words. We introduce and emphasize several new approaches based on Bayesian shrinkage and regularization. We illustrate the relative utility of these approaches with analyses of partisan, gender, and distributive speech in the U.S. Senate.
      

      Slides adapted from 85-94 of Cristian Danescu-Niculescu-Mizil and Lillian Lee, Natural language processing for computational social science, Invited tutorial at NIPS 2016 [alternate link: tutorial announcement, slides]

   2. Similarity measured on the most frequent words ("stop words") only vs. on the content words

   3. 
      

3. Distributional similarity

   (Some others that I don't expect to have time to discuss)

   Type/token ratio.

It represents an intuitively slightly ridiculous null hypothesis that often works surprisingly well as a feature.

Example:

1. BERT vs hand features, controversy paper
2. Word embeddings
   
   1. BERT - word pieces!
3. Language modeling

less hidden