Causal Explanation Analysis on Social Media

Youngseo Son,H Andrew Schwartz,Nipun Bayas

doi:10.18653/v1/d18-1372

Abstract

Understanding causal explanations - reasons given for happenings in one’s life - has been found to be an important psychological factor linked to physical and mental health. Causal explanations are often studied through manual identification of phrases over limited samples of personal writing. Automatic identification of causal explanations in social media, while challenging in relying on contextual and sequential cues, offers a larger-scale alternative to expensive manual ratings and opens the door for new applications (e.g. studying prevailing beliefs about causes, such as climate change). Here, we explore automating causal explanation analysis, building on discourse parsing, and presenting two novel subtasks: causality detection (determining whether a causal explanation exists at all) and causal explanation identification (identifying the specific phrase that is the explanation). We achieve strong accuracies for both tasks but find different approaches best: an SVM for causality prediction (F1 = 0.791) and a hierarchy of Bidirectional LSTMs for causal explanation identification (F1 = 0.853). Finally, we explore applications of our complete pipeline (F1 = 0.868), showing demographic differences in mentions of causal explanation and that the association between a word and sentiment can change when it is used within a causal explanation.

Highlights

Explanations of happenings in one’s life, causal explanations, are an important topic of study in social, psychological, economic, and behavioral sciences
In order to classify whether a message contains causal relation, we compared off-the-shelf Penn Discourse Treebank (PDTB) parsers, linear SVM, RBF SVM, Random forest and LSTM classifiers
We developed a pipeline for causal explanation analysis over social media text, including both causality prediction and causal explanation identification

Summary

Introduction

Explanations of happenings in one’s life, causal explanations, are an important topic of study in social, psychological, economic, and behavioral sciences. To help understand the significance of causal explanations, consider how they are applied to measuring optimism (and its converse, pessimism) (Peterson et al, 1988). In “My parser failed because I always have bugs.”, the emphasized text span is considered a causal explanation which indicates pessimistic personality – a negative event where the author believes the cause is pervasive. In “My parser failed because I barely worked on the code.”, the explanation would be considered a signal of optimistic personality – a negative event for which the cause is believed to be short-lived. Language-based models which can detect causal explanations from everyday social media language can be used for more than automating optimism detection. Language-based assessments would enable other large-scale downstream tasks: tracking prevailing causal beliefs (e.g., about climate change or autism), better extracting process knowledge from non-fiction (e.g., gravity causes objects to move toward one another), or detecting attribution of blame or praise in product or service reviews (“I loved this restaurant because the fish was cooked to perfection”)

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Conclusion