Lillian Lee, Choice 2019 Symposium "Wisdom from Words: Insight from Language and Text Analysis", draft/work in progress
This URL: https://confluence.cornell.edu/display/~ljl2/Choice2019
Applications I and my co-authors have worked on:
For various reasons, including an eye towards deploying applications, we ultimately evaluate our hypothesis with prediction even though we are personally interested and invested in understanding what underlies the phenomenon being considered.
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. |
Chenhao Tan's list of hedging phrases, such as "I suspect", "raising the possibility":This is in the long line of LIWC-like lexicons. [README] [list itself]
Language models, which assign probabilities P(x) to words, sentences or text units after being trained on some language sample.These are great for similarity, distinctiveness, visualization.
Distributional similarity (word embeddings are the modern version)Here's a figure from 1997 about ideas from the early 90's: For references, see the word embeddings section later in this document
Some others I don't expect to have time to discuss
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It represents an intuitively slightly ridiculous null hypothesis that often works surprisingly well as a feature. Example: |
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