Being connected to the local community through a festival mobile application

In this paper we report our investigation into how using and interacting with a local festival mobile app enhanced users’ festival experiences and connected them to other local users and their community. We explored the relationship between users’ perceived basic affordances of mobile technology, perceived opportunities of the festival app, and three elements that sustain the local community — attachment, engagement, and social support networks. Based on the usage logs of 348 active users, as well as survey responses from 80 users, we present a mobile-mediated local community framework and found that engagement is a key mediator of mobile experiences and facets of community

KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction

The political stance prediction for news articles has been widely studied to mitigate the echo chamber effect -- people fall into their thoughts and reinforce their pre-existing beliefs. The previous works for the political stance problem focus on (1) identifying political factors that could reflect the political stance of a news article and (2) capturing those factors effectively. Despite their empirical successes, they are not sufficiently justified in terms of how effective their identified factors are in the political stance prediction. Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Also, to take into account the subtle and important difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by ourselves and learn to fuse the different political knowledge. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.Comment: 12 pages, 5 figures, 10 tables, the Web Conference 2023 (WWW

Use and Adoption Challenges of Wearable Activity Trackers

Wearable activity trackers are becoming widely adopted, yet challenges continue to exist in effective long-term use and adoption. Existing research focuses mostly on the use and adoption challenges associated with technical- or device-related issues and respective workaround strategies. Little is known about how personal preferences and other individual characteristics affect use and adoption of wearable activity trackers. In this paper, we present a six-week user study of 30 users using physical activity trackers embedded in clip-on and smart watch physical devices. We describe novel implications of the usage patterns, including the need to help people be mindful of their physical activity trackers, to understand and further articulate gender differences in use and adoption of wearable devices, to incorporate big data analytics in informing and coaching people’s practices, and to reframe data inaccuracy as a byproduct of mismanagement of expectations of the device’s capabilities and its expected usage.ye

How do you perceive this author? Understanding and modeling authors' communication quality in social media.

In this study, we leverage human evaluations, content analysis, and computational modeling to generate a comprehensive analysis of readers' evaluations of authors' communication quality in social media with respect to four factors: author credibility, interpersonal attraction, communication competence, and intent to interact. We review previous research on the human evaluation process and highlight its limitations in providing sufficient information for readers to assess authors' communication quality. From our analysis of the evaluations of 1,000 Twitter authors' communication quality from 300 human evaluators, we provide empirical evidence of the impact of the characteristics of the reader (demographic, social media experience, and personality), author (profile and social media engagement), and content (linguistic, syntactic, similarity, and sentiment) on the evaluation of an author's communication quality. In addition, based on the author and message characteristics, we demonstrate the potential for building accurate models that can indicate an author's communication quality

Being connected to the local community through a festival mobile application

In this paper we report our investigation into how using and interacting with a local festival mobile app enhanced users’ festival experiences and connected them to other local users and their community. We explored the relationship between users’ perceived basic affordances of mobile technology, perceived opportunities of the festival app, and three elements that sustain the local community — attachment, engagement, and social support networks. Based on the usage logs of 348 active users, as well as survey responses from 80 users, we present a mobile-mediated local community framework and found that engagement is a key mediator of mobile experiences and facets of community

Standardized linear regression coefficients of communication quality with tweet features for the consensus analysis after controlling for reader’s characteristics.

<p>The Durbin-Watson results are close to 2.0, showing that there is no presence of autocorrelation in the residuals. The VIF results for all predictors are less than 4.0. Higher coefficient means less consensus among the respondents.</p

Author- and tweet-based feature comparison between the high and the low communication quality groups at p < 0.05 (sorted by F-values).

<p>Author- and tweet-based feature comparison between the high and the low communication quality groups at p < 0.05 (sorted by F-values).</p

Standardized linear regression coefficients of authors’ communication quality with evaluators’ (readers’) gender, age, social media use length, frequency, and the five features of personality.

<p>The Durbin-Watson results are close to 2.0, showing that there is no presence of autocorrelation in the residuals. The VIF results for all predictors are less than 4.0.</p

Standardized linear regression coefficients of authors’ communication quality with linguistic characteristics from LIWC and features unique in social media.

<p>The Durbin-Watson results are close to 2.0, showing that there is no presence of autocorrelation in the residuals. The VIF results for all predictors are less than 4.0.</p

Top ten important features from the RF model.

<p>Top ten important features from the RF model.</p