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Language, Perception, and Causal Inference in Online Communication
Title
Language, Perception, and Causal Inference in Online Communication
Creator
Wang, Zhao
Date
2021
Description
With the proliferation of social media platforms, online communication is becoming increasingly popular. The nature of a wide audience and...
Show moreWith the proliferation of social media platforms, online communication is becoming increasingly popular. The nature of a wide audience and rapid spread of information make these platforms attractive to public entities, organizations, and individuals. Marketers use these platforms to advertise their products and collect customer feedbacks (e.g. Amazon, Airbnb, Yelp, IMDB). Politicians use these platforms to directly speak with the public and canvass for votes (e.g., Twitter, Youtube, Snapchat). Individuals use these platforms to connect with friends and share daily life (e.g., Twitter, Facebook, Instagram, Weibo). The various platforms allow users to build public image and increase reputation through a fast and cheap way. However, due to the lack of regulations and low effort of online communication, some users try to manage their public impression using vague and tricky expressions during communication, making it hard for the audience to identify the authenticity of the public messages. Studies across many disciplines have shown that words and language play an important role in effective communication but the nature and extent of this role remain murky. Prior works have investigated wording effect on audience perception, but we still need automatic methods to estimate the causal effect of lexical choice on human perception in large scale. Getting insights into the treatment effect of subtle linguistic signals is crucial for intelligent language understanding and text analysis.The causal estimation of wording effect on perception also provides us an alternative way to understand the causal relationship between word features and perception labels. Comparing with correlational associations between features and labels, which is typically learned by statistical machine learning models, we find inconsistencies between the causal and correlational associations. These inconsistencies suggest possible spurious correlations in text classification and it's significant to address this issue by applying causal inference knowledge to guide statistical classifiers.In this thesis, our first goal is to investigate wording effect in online communication and study causal inference in text. We start from a deceptive marketing task to quantify entities' word commitment from online public messaging and identify potentially inauthentic entities. We then propose several frameworks to estimate the causal effects of word choice on audience perception by adapting Individual Treatment Effect estimation from causal inference literature to our problem of Lexical Substitution Effect estimation. The findings from these projects motivate us to explore our second goal of applying causal inference knowledge to improve statistical model robustness. Specifically, we study the causal and correlational associations in text and discover possible spurious correlations in text classifiers. Then, by extending the causal discovery, we propose two frameworks to improve text classifier robustness and fairness either by directly removing bias correlations or by training a robust model with automatically generated counterfactual samples.
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A Case of "Gray Plagiarism" from the History of the History of Computing
Title
A Case of "Gray Plagiarism" from the History of the History of Computing
Creator
Davis, Michael
Date
2006, 2006
Publisher
Plagiary : Cross-Disciplinary Studies in Plagiarism, Fabrication, and Falsfication
Description
Claiming as one's own what one knows to be the discovery of another is certainly plagiarism. But what about merely failing to acknowledge the...
Show moreClaiming as one's own what one knows to be the discovery of another is certainly plagiarism. But what about merely failing to acknowledge the work of another where one does not give the impression that the discovery is one's own? Does it matter how easy it was to make the discovery? This paper analyzes a case in this gray area in academic ethics. The focus is not on the failure to attribute itself but on the attempt of an independent scholar who, believing himself to be the victim of "gray plagiarism”, sought a forum in which to make his complaint. The story could be told from several perspectives. I shall tell it primarily from the perspective of the complainant, an outsider, because I believe that way of telling it best reveals the need to think more deeply about how we (acting for the universities to which we belong) assign credit, especially to scholars outside, and about how we respond when someone complains of a failure to assign credit. My purpose is not to indict individuals but to change a system. This paper updates a case I first described in 1993.
Davis, M.  (2006).  “Gray Plagiarism”: A Case from the History of the History of Computing.  Plagiary:  Cross‐Disciplinary Studies in Plagiarism, Fabrication, and Falsification, 1 (7):  1‐18.   
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