activity
20172023
most citedThrough a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users

142 citations · 280 across the 14 of their papers we have counts for

collaborators

14 papers

cs.HC2023★ 2 cited

Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets

Yawen Guo, Rachael Zehrung, Katie Genuario +4

Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care i…

cs.CY2023★ 1 cited

Emoji Promotes Developer Participation and Issue Resolution on GitHub

Yuhang Zhou, Xuan Lu, Ge Gao +2

Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-ver…

cs.LG2023

Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic

Xuan Lu, Wei Ai, Yixin Wang +1

While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this an…

cs.LG2021★ 12 cited

Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub

Xuan Lu, Wei Ai, Zhenpeng Chen +2

Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more em…

cs.CY2021★ 4 cited

Understanding Diffusion of Recurrent Innovations

Fuqi Lin

The diffusion of innovations theory has been studied for years. Previous research efforts mainly focus on key elements, adopter categories, and the process of innovation diffusion.…

cs.SE2019★ 49 cited

SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering

Zhenpeng Chen, Yanbin Cao, Xuan Lu +2

Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A f…

cs.SI2019★ 2 cited

A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data

Huoran Li

User mobility prediction is widely considered to be helpful for various sorts of location based services on mobile devices. A large amount of studies have explored different algori…

cs.CY2018★ 8 cited

A First Look at Emoji Usage on GitHub: An Empirical Study

Xuan Lu, Yanbin Cao, Zhenpeng Chen +1

Emoji is becoming a ubiquitous language and gaining worldwide popularity in recent years including the field of software engineering (SE). As nonverbal cues, emojis are widely used…

cs.IR2018★ 6 cited

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Zhenpeng Chen, Sheng Shen, Ziniu Hu +3

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more Engl…

cs.SE2017★ 2 cited

Mining Device-Specific Apps Usage Patterns from Large-Scale Android Users

Huoran Li, Xuan Lu

When smartphones, applications (a.k.a, apps), and app stores have been widely adopted by the billions, an interesting debate emerges: whether and to what extent do device models in…

cs.SE2017

PRADA Applicability in Industrial Practice

Xuan Lu

The proliferation of Android devices brings the fragmentation problem. Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and…

cs.SE2017★ 5 cited

Towards Release Strategy Optimization for Apps in Google Play

Sheng Shen, Xuan Lu, Ziniu Hu

In the appstore-centric ecosystem, app developers have an urgent requirement to optimize their release strategy to maximize the success opportunity of their apps. To address this p…

cs.HC2017★ 142 cited

Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users

Zhenpeng Chen, Xuan Lu, Wei Ai +3

Based on a large data set of emoji using behavior collected from smartphone users over the world, this paper investigates gender-specific usage of emojis. We present various intere…

cs.CY2017★ 47 cited

Mining Behavioral Patterns from Millions of Android Users

Xuanzhe Liu, Huoran Li, Xuan Lu +4

The prevalence of smart mobile devices has promoted the popularity of mobile applications (a.k.a. apps). Supporting mobility has become a promising trend in software engineering re…