Don't Let Me Be Misunderstood: Comparing Intentions and Perceptions in Online Discussions

Discourse involves two perspectives: a person's intention in making an utterance and others' perception of that utterance. The misalignment between these perspectives can lead to undesirable outcomes, such as misunderstandings, low productivity and even overt strife. In this work, we present a computational framework for exploring and comparing both perspectives in online public discussions. We combine logged data about public comments on Facebook with a survey of over 16,000 people about their intentions in writing these comments or about their perceptions of comments that others had written. Unlike previous studies of online discussions that have largely relied on third-party labels to quantify properties such as sentiment and subjectivity, our approach also directly captures what the speakers actually intended when writing their comments. In particular, our analysis focuses on judgments of whether a comment is stating a fact or an opinion, since these concepts were shown to be often confused. We show that intentions and perceptions diverge in consequential ways. People are more likely to perceive opinions than to intend them, and linguistic cues that signal how an utterance is intended can differ from those that signal how it will be perceived. Further, this misalignment between intentions and perceptions can be linked to the future health of a conversation: when a comment whose author intended to share a fact is misperceived as sharing an opinion, the subsequent conversation is more likely to derail into uncivil behavior than when the comment is perceived as intended. Altogether, these findings may inform the design of discussion platforms that better promote positive interactions.Comment: Proceedings of The Web Conference (WWW) 202

Identifying Impact Factors of Question Quality in Online Health Q&A Communities: an Empirical Analysis on MedHelp

Online health Q&A communities help patients, doctors and other users conveniently search and share healthcare information online and have gained much popularity all over the world. Good-quality questions that raise massive discussions could trigger users’ engagement online, which is beneficial for platform operation. However, little attention has been paid to the antecedents of question quality in online health Q&A communities. To have a deep investigation of healthcare question quality, this research aims to investigate the impact factors from two special aspects that are neglected in previous research, i.e., user’s structural influence and questions’ sentiment. Using a dataset collected from MedHelp, one of the largest online health Q&A communities, we found that users with high structural influences and questions with negative sentiment have positive associations with the answer number of questions. Our research would offer meaningful suggestions to platform managers and users

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Objectivity: its meaning, its limitations, its fateful omissions

In this text, we explore the guiding thread of the volume "Objectivity after Kant" by first discussing how the main question pertaining to transcendental objectivity arose at the Centre for Critical Philosophy. This exposition takes the form of a microhistorical genealogy, from which the main ideas pursued in the research conducted at this Centre can be distilled. In the second part, we briefly sketch how the different contributors have addressed this question. Its purpose is to facilitate the reader’s navigation through the variety of topics and perspectives addressed throughout this volume, and incite further reflection on the central issue it pursues

Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.Comment: The 2017 Annual Meeting of the Association for Information Science and Technology (ASIST