Esra de Groot

Esra de Groot

PhD Candidate

Posts by Collection

portfolio

publications

The Impact of a Crisis Event on Predicting Social Media Virality

Published in Complex Networks XIV, Springer Proceedings in Complexity, 2023

Abstract: Understanding why specific information pieces become viral in online social networks is a fundamental step in governing social media platforms. While previous studies explored which user and network properties enable information virality, little is known about how offline crisis events impact these features. Here we investigate to what extent a crisis event impacts virality prediction models. We analyze Twitter data before and after the 2022 Russian invasion of Ukraine as a case study. We train and test statistical learning models on data before and after the invasion, evaluating which features related to content (e.g., existence of an URL), the user (e.g., number of previous tweets), or the network (e.g., degree centrality) are most relevant to predict virality. Tweets with more than 100 retweets are considered viral. We observe that the crisis event affects virality prediction: predictive accuracy is reduced when a model trained on data before the invasion is evaluated on data after the invasion—instead of being evaluated on data before the invasion. We observe that the crisis event leads to an increased fraction of viral tweets by users with fewer followers after the invasion. Moreover, we observe that, after the invasion, tweets containing URLs are more likely to become viral, implying an increased interest in website links, photos, and videos. Overall, our study reveals a shift in the importance of features underlying virality as a result of an offline crisis event.

Recommended citation: de Groot, E. C. S., Pillai, R. G., & Santos, F. P. (2023). "The Impact of a Crisis Event on Predicting Social Media Virality." Complex Networks XIV, Springer Proceedings in Complexity. (pp. 95-107). http://sudegroot.github.io/files/deGroot_Virality.pdf

"Are we all in the same boat?" Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work

Published in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), 2024

Abstract: Human intelligence continues to be essential in building ground-truth data, training sets, and for evaluating a plethora of systems. The democratized and distributed nature of online crowd work — an attractive and accessible feature that has led to the proliferation of the paradigm — has also meant that crowd workers may not always feel connected to their remote peers. Despite the prevalence of collaborative crowdsourcing practices, workers on many microtask crowdsourcing platforms work on tasks individually and are seldom directly exposed to other crowd workers. In this context, improving worker engagement on microtask crowdsourcing platforms is an unsolved challenge. At the same time, fostering a sense of community among workers can improve the sustainability and working conditions in crowd work. This work aims to increase worker engagement in conversational microtask crowdsourcing by leveraging evolving avatars that workers can customize as they progress through monotonous task batches. We also aim to improve group identification in individual tasks by creating a community space where workers can share their avatars and feelings on task completion. To this end, we carried out a preregistered between-subjects controlled study (N = 680) spanning five experimental conditions and two task types. We found that evolving and customizable worker avatars can increase worker retention. The prospect of sharing worker avatars and task-related feelings in a community space did not consistently affect group identification. Our exploratory analysis indicated that workers who identify themselves as crowd workers experienced greater intrinsic motivation, subjective engagement, and perceived workload. Furthermore, we discuss how task differences shape the relative effectiveness of our interventions. Our findings have important theoretical and practical implications for designing conversational crowdsourcing tasks and in shaping new directions for research to improve crowd worker experiences.

Recommended citation: Esra Cemre Su de Groot and Ujwal Gadiraju. 2024. ""Are we all in the same boat?" Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work." In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA. http://sudegroot.github.io/files/CHI2024_Avatars.pdf

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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