Huahai Yang

System U: automatically deriving personality traits from social media for people recommendation

This paper presents a system, System U, which automatically derives peoples personality traits from social media and recommends people for different tasks. The system leverages linguistic signals appearing in a persons social media activities to compute the personality portraits including Big Five personality, fundamental needs and basic human values. This system and technology can be used in a wide variety of personalized applications, such as recommending people to answer questions.

Analyzing the quality of information solicited from targeted strangers on social media

The emergence of social media creates a unique opportunity for developing a new class of crowd-powered information collection systems. Such systems actively identify potential users based on their public social media posts and solicit them directly for information. While studies have shown that users will respond to solicitations in a few domains, there is little analysis of the quality of information received. Here we explore the quality of information solicited from Twitter users in the domain of product reviews, specifically reviews for a popular tablet computer and L.A.-based food trucks. Our results show that the majority of responses to our questions (>70%) contained relevant information and often provided additional details (>37%) beyond the topic of the question. We compare the solicited Twitter reviews to other user-generated reviews from Amazon and Yelp, and found that the Twitter answers provided similar information when controlling for the questions asked. Our results also reveal limitations of this new information collection method, including its suitability in certain domains and potential technical barriers to its implementation. Our work provides strong evidence for the potential of this new class of information collection systems and design implications for their future use.

Understand users’ comprehension and preferences for composing information visualizations

We are developing an automated visualization system that helps users combine two or more existing information graphics to form an integrated view. To establish empirical foundations for building such a system, we designed and conducted two studies on Amazon Mechanical Turk to understand users’ comprehension and preferences of composite visualization under different conditions (e.g., data and tasks). In Study 1, we collected more than 1,500 textual descriptions capturing about 500 participants’ insights of given information graphics, which resulted in a task-oriented taxonomy of visual insights. In Study 2, we asked 240 participants to rank composite visualizations by their suitability for acquiring a given visual insight identified in Study 1, which resulted in ranked user preferences of visual compositions for acquiring each type of insight. In this article, we report the details of our two studies and discuss the broader implications of our crowdsourced research methodology and results to HCI-driven visualization research.

OpinionBlocks: A Crowd-Powered, Self-improving Interactive Visual Analytic System for Understanding Opinion Text

Millions of people rely on online opinions to make their decisions. To better help people glean insights from massive amounts of opinions, we present the design, implementation, and evaluation of OpinionBlocks, a novel interactive visual text analytic system. Our system offers two unique features. First, it automatically creates a fine-grained, aspect-based visual summary of opinions, which provides users with insights at multiple levels. Second, it solicits and supports user interactions to rectify text-analytic errors, which helps improve the overall system quality. Through two crowd-sourced studies on Amazon Mechanical Turk involving 101 users, OpinionBlocks demonstrates its effectiveness in helping users perform real-world opinion analysis tasks. Moreover, our studies show that the crowd is willing to correct analytic errors, and the corrections help improve user task completion time significantly.

Using Personal Traits For Brand Preference Prediction

In this paper, we present a comprehensive study of the relationship between an individual’s personal traits and his/her brand preferences. In our analysis, we included a large number of character traits such as personality, personal values and individual needs. These trait features were obtained from both a psychometric survey and automated social media analytics. We also included an extensive set of brand names from diverse product categories. From this analysis, we want to shed some light on (1) whether it is possible to use personal traits to infer an individual’s brand preferences (2) whether the trait features automatically inferred from social media are good proxies for the ground truth character traits in brand preference prediction.

Computational discovery of personal traits from social multimedia

Hundreds of millions of people leave digital footprints, including textual posts and photos, on public on social media and social networking sites. Here we present our work on using these digital footprints-social multimedia content-to derive four types of basic personal traits for individuals. Moreover, we show how these basic traits can be used in combinations to help assess composite traits in two cases: trust modeling and resilience modeling. We share our preliminary results and discuss future research directions.