On Left and Right: Understanding the Discourse of Presidential Election in Social Media Communities

As a promising platform for political discourse, social media becomes a battleground for presidential candidates as well as their supporters and opponents. Stance detection is one of the key tasks in the understanding of political discourse. However, existing methods are dominated by supervised techniques, which require labeled data. Previous work on stance detection is largely conducted at the post or user level. Despite that some studies have considered online political communities, they either only select a few communities or assume the stance coherence of these communities. Political party extraction has rarely been addressed explicitly. To address the limitations, we developed an unsupervised learning approach to political party extraction and stance detection from social media discourse. We also analyzed and compared (sub)communities with respect to their characteristics of political stances and parties. We further explored (sub)communities’ shift in political stance after the 2020 US presidential election

From Small to Big: Smartwatch Use in Mitigating COVID-19 – Understanding User Experience from Social Media Content Analysis

Smartwatches offer both functions and convenience that can have great potentials for technological interventions. Despite widespread discussion of technological interventions for COVID-19, smartwatch use has received little attention in the literature. This research aims to fill the literature gap by providing a broad understanding of smartwatch use for COVID-19 mitigation. We investigate smartwatch use through content analysis of the data collected from two social media platforms. The method allows us to draw on user experience beyond technological features and functions. In addition to functions, we also identified the concerns of using smartwatches for mitigating COVID-19. Furthermore, we uncovered both similarities and differences between the different social media platforms in terms of functions and concerns of smartwatch use. Our findings have implications for various stakeholders of the smartwatch technology and for mitigating the impact of the pandemic

How Does User Engagement Support Content Moderation? A Deep Learning-based Comparative Study

Content moderation is a common intervention strategy for reviewing user-generated content on social media platforms. Engaging users in content moderation is promising for making ethical and fair moderation decisions. A few studies that have considered user engagement in content moderation have primarily focused on classifying user-generated comments, rather than leveraging the information of user engagement to make a moderation decision on user-generated posts. Moreover, how to extract information from user engagement to enhance content moderation remains unclear. To address the above-mentioned limitations, this study proposes a framework for user engagement-enhanced moderation of user-generated posts. Specifically, it incorporates the credibility and stance of user-generated content into graph learning. Our empirical evaluation shows that the models based on our proposed framework outperform the state-of-the-art deep learning models in making moderation decisions for user-generated posts. The findings of this study have implications for augmenting the moderation of social media content and for improving the safety and success of online communities

What is More Important for Touch Dynamics based Mobile User Authentication?

Mobile user authentication (MUA) has become a gatekeeper for securing a wealth of personal and sensitive information residing on mobile devices. Keystrokes and touch gestures are two types of touch behaviors. It is not uncommon for a mobile user to make multiple MUA attempts. Nevertheless, there is a lack of an empirical comparison of different types of touch dynamics based MUA methods across different attempts. In view of the richness of touch dynamics, a large number of features have been extracted from it to build MUA models. However, there is little understanding of what features are important for the performance of such MUA models. Further, the training sample size of template generation is critical for real-world application of MUA models, but there is a lack of such information about touch gesture based methods. This study is aimed to address the above research limitations by conducting experiments using two MUA prototypes. Their empirical results can not only serve as a guide for the design of touch dynamics based MUA methods but also offer suggestions for improving the performance of MUA models