Visual analysis of online social media to open up the investigation of stance phenomena

Abstract

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

The State of the Art in Sentiment Visualization

“…uVSAT by Kucher et al . [KSBK*16] provides a separate representation of individual documents based on selected subsets of aggregated emotion/stance value series initially presented to the user (see Figure (b)). Ruppert et al .…”

“…Image courtesy of Florence Ying Wang . (b) uVSAT [KSBK*16] provides a detailed view of a selected document for the purposes of stance analysis. (c) IdeaFlow [WLC*16] uses sentiment analysis results for the lead‐lag analysis of ideas.…”

“…uVSAT by Kucher et al . [KSBK*16] is a VA system dedicated to sentiment and stance analysis which detects the expressions of Ekman's six emotions as well as certainty and uncertainty in social media data over time. These time series are visualized with line plots, and the contents of corresponding documents are represented with highlighted text, scatter plot thumbnails, and bubble charts, as displayed in Figure (b).…”

“…For example, the category Region of Interest includes techniques which support automatic detection and highlighting of interesting or anomalous items [CQZ*12, ZCW*14, ZGWZ14, CSL*16, LSB*16], regions [KSBK*16], or peaks [MBB*11, SHHW*12] (overall, 8% of the total set of techniques). Time Density Plots by Rohrdantz et al .…”

Annotating Speaker Stance in Discourse: The Brexit Blog Corpus

“…They observed that sentiment features improve the stance classification results, which indicates that knowledge of the sentiment in a tweet facilitates the identification of stance. In their study, Kucher et al (2016b) described an approach for stance analysis based on sentiment or certainty considerations and presented the uVSAT tool for visual stance analysis, the analysis of temporal and textual data, and the exportation of stance markers in order to prepare a stance-oriented training data set.…”