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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Dr. Daejin Choi
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
Published:
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Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
datasets
Rumor Echo Chamber: An Amplifier of Rumor Spread in Social Media
Daejin Choi, Selin Chun, Hyunchul Oh, Jinyoung Han, and Taekyoung ‘Ted’ Kwon
Preventing rumor spread with deep learning
Daejin Choi, Hyuncheol Oh, Selin Chun, Taekyoung Kwon, and Jinyoung Han
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
talks
[Conference Talk] Characterizing Online Conversations on Reddit:From the Perspectives of Content Properties and User Participation Behaviors
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It becomes the norm for people to communicate with one another through various online social channels, where different conversation structures are formed depending on platforms. One of the common online communication patterns is a threaded conversation where a user brings up a conversation topic, and then other people respond to the initiator or other participants by commenting, which can be modeled as a tree structure. In this talk, I introduce the work which investigates (i) the characteristics of online threaded conversations in terms of volume, responsiveness, and virality and (ii) what and how content properties and user participation behaviors are associated with such characteristics. To this end, we collect 700 K threaded conversations from 1.5 M users in Reddit, one of the most popular online communities allowing people to communicate with others in the form of threaded conversations. Using the collected dataset, we find that ‘social’ words, difficulties of texts, and document relevancy are associated with the volume, responsiveness, and virality of conversations. We also discover that large, viral conversations are mostly formed by a small portion of users who are reciprocally communicate with others by analyzing user interactions. Our analysis on discovering user roles in conversations reveal that users who are interested in multiple topics play important roles in large and viral conversations, whereas heavy posting users play important roles in responsive conversations. We expand our analysis to topical communities (i.e., subreddits) and find that news-related, image-based, and discussion-related communities are more likely to have large, responsive, and viral conversations, respectively.
[Conference Talk] Predicting Popular and Viral Image Cascades in Pinterest
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The word-of-mouth diffusion has been regarded as an important mechanism to advertise a new idea, image, technology, or product in online social networks (OSNs). In this talk, I introduce the study on the prediction of popular and viral image diffusion in Pinterest. We first characterize an image cascade from two perspectives: (i) volume – how large the cascade is, i.e., total number of users reached, and (ii) structural virality – how many users in the cascade are responsible for attracting other users. Our model predicts whether an image will be (a) popular in terms of the volume of its cascade, or (b) viral in terms of the structural virality. Our analysis reveals that a popular image is not necessarily viral, and vice versa. This motivates us to investigate whether there are distinctive features for accurately predicting popular or viral image cascades. To predict the popular or viral image cascades, we consider the following feature sets: (i) deep image features, (ii) image meta and poster’s information, and (iii) initial propagation pattern. We find that using deep image features alone is not as effective in predicting popular or viral image cascades. We show that image meta and poster’s information are strong predictors for predicting popular image cascades while image meta and initial propagation patterns are useful to predict viral image cascades. We believe our exploration can give an important insight for content providers, OSN operators, and marketers in predicting popular or viral image diffusion.
[Invited Talk] Computational Social Science with Graph Modeling
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Introducing basic graph theroy and how the theory and its techniques can be used in state-of-art research on Computational Social Science.
[Invited Talk] Data Science & Machine Learning Tutorial
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Introducing theory and techniques of data analysis and machine learning, from traditional to state-of-art, and the research on machine-learning-based applications in Social Science.
[Conference Talk] Supporters First: Understanding Online Social Support from a Supporter Perspective
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Social support or peer support in mental health has successfully settled down in online spaces by reducing the potential risk of critical mental illness (e.g., suicidal thoughts) of support-seekers. While the prior work has mostly focused on support-seekers, particularly investigating their behavioral characteristics and the effects of online social supports to support-seekers, this paper seeks to understand online social support from supporters’ perspectives, who have informational or emotional resources that may affect support-seekers either positively or negatively. To this end, we collect and analyze a large-scale of dataset consisting of the supporting comments and their target posts from 55 mental health communities in Reddit. We also develop a deep-learning-based model that scores informational and emotional support to the supporting comments. Based on the collected and scored dataset, we measure the characteristics of the supporters from the behavioral and content perspectives, which reveals that the supporters tend to give emotional support than informational support and the atmosphere of social support communities tend also to be emotional. We also understand the relations between the supporters and the support-seekers by giving a notion of “social supporting network’’, whose nodes and edges are the sets of the users and the supporting comments. Our analysis on top users by out-degrees and in-degrees in social supporting network demonstrates that heavily-supportive users are more likely to give informational support with diverse content while the users who attract much support exhibit continuous support-seeking behaviors by uploading multiple posts with similar content. Lastly, we identified structural communities in social supporting network to explore whether and how the supporters and the support-seeking users are grouped. By conducting topic analysis on both the support-seeking posts and the supporting comments of individual communities, we revealed that small communities deal with a specific topic such as hair-pulling disorder. We believe that the methodologies, dataset, and findings can not only expose more research questions on online social supports in mental health, but also provide insight on improving social support in online platforms.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.