Making Sense of Social Events by Event monitoring, Visualization and Underlying Community Profiling

With the prevalence of intelligent devices, social networks have been playing an increasingly important role in our daily life. Various social networks (e.g., Twitter, Facebook) provide convenient platforms for users to explore the world. In this thesis, we study the problem of multi-perspective analysis of social events detected from social networks. In particular, we aim to make sense of the social events from the following three perspectives: 1) what are these social events about; 2) how do these events evolve along timeline; 3) who are involved in the discussions on these events. We mainly work on two categories of social data: the user-generated contents such as tweets and Facebook posts, and the users' interactions such as the follow and reply behaviours among users. On one hand, the posts reveal valuable information that describes the evolutions of miscellaneous social events, which is crucial for people to understand the world. On the other hand, users' interactions demonstrate users' relationships among each other and thus provide opportunities for analysing the underlying communities behind the social events. However, it is not practical to manually detect social events, monitor event evolutions or profile the underlying communities from the massive amount of social data generated everyday. Hence, how to efficiently and effectively extract, manage and analyse the useful information from the social data for multi-perspective social events understanding is of great importance. The social data is dynamic source of information which enables people to stay informed of what is happening now and who are the active and influential users discussing these social events. For one thing, social data is generated by people worldwide at all time, which may make fast identification of events even before the mainstream media. Moreover, the continuous stream of social data reflects the event evolutions and characterizes the events with changing opinions at different stages. This provides an opportunity to people for timely responses to urgent events. For another, users are often not isolated in social networks. The interactions between users can be utilized to discover the communities who discuss each social event. Underlying community profiling provides answers to the questions like who are interested in these events, and which group of people are the most influential users in spreading certain event topics. These answers deepen our understanding of the social events by considering not only the events themselves but also the users behind these events. The first research task in this thesis is to monitor and index the evolving events from social textual contents. The social data cover a wide variety of events which typically evolve over time. Although event detection has been actively studied, most existing approaches do not track the evolution of events, nor do they address the issue of efficient monitoring in the presence of a large number of events. In this task, we detect events based on the user-generated textual contents and design four event operations to capture the dynamics of events. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List, to manage dynamic event databases for the acceleration of large-scale event search and update. The second research task is to explore multiple features for social events tracking and visualization. In addition to textual contents utilized in the first task, social data contains various features, such as images and timestamps. The benefits of incorporating different features into event detection are twofold. First, these features provide supplemental information that facilitates the event detection model. Second, different features describe the detected events from different aspects, which enables users to have a better understanding with more vivid visualizations. To improve the event detection performance, we propose a novel generative probabilistic model which jointly models five different features. The event evolution tracking is achieved by applying the maximum-weighted bipartite graph matching on the events discovered in consecutive periods. Events are then visualized by the representative images selected based on our three defined criteria. The third research task is to detect and profile the underlying social communities in social events. The social data not only contains user-generated contents which describe the events evolutions, but also comprises various information on the users who discuss these events, such as user attributes, user behaviours, and so on. Comprehensively utilizing this user information can help to group similar users into communities, and enrich the social event analysis from the community perspective. Motivated by the rich semantics about user behaviours hidden in social data, we extend the community definition as a group of users who are not only densely connected, but also having similar behaviours. Moreover, in addition to detecting the communities, we further profile each of the detected communities for social events analysis. A novel community profiling model is designed to detect and characterize a community by both content profile (what a community is about) and diffusion profile (how it interacts with others)

APLIKASI REKOMENDASI TAG PADA SITUS BERBAGI GAMBAR FLICKR®

Dalam sebuah situs berbagi gambar, ketika pengguna akan mengunggah sebuah gambar, biasanya akan memberikan tag/keterangan pada gambar tersebut. Tujuannya agar gambar dapat ditemukan kembali oleh pemiliknya atau oleh pengguna lain yang memiliki minat yang sama terhadap topik gambar tersebut. Aplikasi ini menerapkan teknik Asossiation Rule Miningpada tag sebuah situs berbagi gambar untuk memberikan rekomendasi pada pengunggah gambar tentang tag lainyang dapat ditambahkan pada gambar yang akan diunggah berdasarkan pada pasangan tag yang sering muncul pada database gambar yang ada, dengan mengasumsikan tag yang digunakan adalah dalam bahasa Inggris. Dalam percobaan berskala kecil ini, diambil tag awal “animal” sebagai topik utama pencarian untuk kemudian dicari tag lain yang berasosiasi dengantag tersebut. Aplikasi ini dapat menghasilkan rekomendasi tag lain ketika pengguna melakukan pencarian sebuah tag yang bertemakan “animal”. Keywords:rekomendasi tag, association rule mining, flick

Friend Ranking in Online Games via Pre-training Edge Transformers

Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper lost friend ranking list essentially. Traditional friend recall methods focus on rules like friend intimacy or training a classifier for predicting lost players' return probability, but ignore feature information of (active) players and historical friend recall events. In this work, we treat friend recall as a link prediction problem and explore several link prediction methods which can use features of both active and lost players, as well as historical events. Furthermore, we propose a novel Edge Transformer model and pre-train the model via masked auto-encoders. Our method achieves state-of-the-art results in the offline experiments and online A/B Tests of three Tencent games.Comment: Accepted by the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023

The Role of School Adaptation and Self-Concept in Influencing Chinese High School Students’ Growth in Math Achievement

A longitudinal designed research study was conducted to provide empirical evidence regarding the influences of three dimensions of students’ school adaptation on their math achievement growth over the first year of high school. These dimensions included learning adaptation, stress management, and personal communication. Student math achievement growth was measured using the student growth percentile (SGP) score. Structural equation modeling (SEM) was used to test for the possible mediating role of self-concept behind those three relationships. Based on the model comparison, it was discovered that school adaptation significantly and positively influences student math achievement growth via mediating effects of student academic self-concept, as opposed to showing a direct impact on students. The findings of this study have important implications for educators and parents to aid students in their pursuit of academic success

APLIKASI REKOMENDASI TAG PADA SITUS BERBAGI GAMBAR FLICKR®

Abstrak. Dalam sebuah situs berbagi gambar, ketika pengguna akan mengunggah sebuah gambar, biasanya akan memberikan tag/keterangan pada gambar tersebut. Tujuannya agar gambar dapat ditemukan kembali oleh pemiliknya atau oleh pengguna lain yang memiliki minat yang sama terhadap topik gambar tersebut. Aplikasi ini menerapkan teknik Asossiation Rule Mining pada tag sebuah situs berbagi gambar untuk memberikan rekomendasi pada pengunggah gambar tentang tag lain yang dapat ditambahkan pada gambar yang akan diunggah berdasarkan pada pasangan tag yang sering muncul pada database gambar yang ada, dengan mengasumsikan tag yang digunakan adalah dalam bahasa Inggris. Dalam percobaan berskala kecil ini, diambil tag awal “animal” sebagai topik utama pencarian untuk kemudian dicari tag lain yang berasosiasi dengan tag tersebut. Aplikasi ini dapat menghasilkan rekomendasi tag lain ketika pengguna melakukan pencarian sebuah tag yang bertemakan “animal”. Kata kunci: rekomendasi tag, association rule mining, flick

Community Structure Characterization

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry

ErbB2 Signaling Increases Androgen Receptor Expression in Abiraterone-Resistant Prostate Cancer

Purpose: ErbB2 signaling appears to be increased and may enhance AR activity in a subset of CRPC, but agents targeting ErbB2 have not been effective. This study was undertaken to assess ErbB2 activity in abiraterone-resistant prostate cancer (PCa), and determine whether it may contribute to androgen receptor (AR) signaling in these tumors. Experimental Design: AR activity and ErbB2 signaling were examined in the radical prostatectomy specimens from a neoadjuvant clinical trial of leuprolide plus abiraterone, and in the specimens from abiraterone-resistant CRPC xenograft models. The effect of ErbB2 signaling on AR activity was determined in two CRPC cell lines. Moreover, the effect of combination treatment with abiraterone and an ErbB2 inhibitor was assessed in a CRPC xenograft model. Results: We found that ErbB2 signaling was elevated in residual tumor following abiraterone treatment in a subset of patients, and was associated with higher nuclear AR expression. In xenograft models, we similarly demonstrated that ErbB2 signaling was increased and associated with AR reactivation in abiraterone-resistant tumors, while ERBB2 message level was not changed. Mechanistically, we show that ErbB2 signaling and subsequent activation of the PI3K/AKT signaling stabilizes AR protein. Inhibitors targeting ErbB2/PI3K/AKT pathway disrupt AR transcriptional activity. Furthermore, concomitantly treating CRPC xenograft with abiraterone and an ErbB2 inhibitor, lapatinib, blocked AR reactivation and suppressed tumor progression. Conclusions: ErbB2 signaling is elevated in a subset of abiraterone-resistant prostate cancer patients and stabilizes AR protein. Combination therapy with abiraterone and ErbB2 antagonists may be effective for treating the subset of CRPC with elevated ErbB2 activity