Daniel Dajun Zeng
Biographic Data
| ID | 1911632 |
|---|---|
| NAME | Daniel Dajun Zeng |
| GIVEN NAMES | Daniel Dajun |
| FAMILY NAME | Zeng |
| SIGNATURE | ZENG D D |
| AFFILIATIONS | Chinese Academy of Sciences |
| ORCID | 0000-0002-9046-222X |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 14 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Graph Representation Learning of Multilayer Spatial–Temporal Networks for Stock Predictions
Accurate stock market prediction is crucial for investors seeking significant profits. With increased economic activity, various interrelations between listed companies have become important for accurate predictions. These relations can be represented as complex financial networks, aiding the development of effective graph neural network (GNN) prediction methods. However, current GNN-based methods for stock prediction typically rely on a single s…
Social Cognition-Enhanced Public Opinion Response During Emergencies
CGNN
With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous …
Towards Human–Machine Recognition Alignment
The multimodality nature of web data has necessitated complex multimodal information retrieval for a wide range of web applications. Deep neural networks (DNNs) have been widely employed to extract semantic features from raw samples to improve retrieval accuracy. In addition, hashing is widely used to improve computational and storage efficiency. As such, deep hashing frameworks have been applied for multimodal retrieval tasks. However, there is …
A Deep Learning Approach for Semantic Analysis of Covid-19-Related Stigma on Social Media
The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the earl…
Hashing Fake
The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semanti…
Game Starts at GameStop
In January 2021, the users of subreddit r/wallstreetbets (WSB) triggered an unprecedented short squeeze by driving up GameStop’s stock price to an unimaginable high point. During the event, a large number of users participated in the discussion about GameStop and coordinated trading behavior on r/WSB to push the stock price higher. In this article, we investigate the characteristics of the collective behaviors and social dynamics from the evoluti…
Equitable access to Covid-19 vaccines makes a life-saving difference to all countries
Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HIC…
Aggregate effects of advertising decisions
Purpose The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search…
Mining online e-liquid reviews for opinion polarities about e-liquid features
We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users
Analysis of symptoms and their potential associations with e-liquids’ components
E-liquid components shown to be associated with e-cigarette use symptomology were VG/PG ratios, flavors, and nicotine levels. Future analysis could be conducted based on the structure of e-liquid components categories built in this study. Information revealed in this study could be utilized by e-cigarette users to understand the relationship between e-liquid type and symptoms experienced, by vendors to choose appropriate recipes of e-liquid, and …
An Examination of Electronic Cigarette Content on Social Media
In recent years, the emerging electronic cigarette (e-cigarette) marketplace has shown great development prospects all over the world. Reddit, one of the most popular forums in the world, has a very large user group and thus great influence. This study aims to gain a systematic understanding of e-cigarette flavors based on data collected from Reddit. Flavor popularity, mixing, characteristics, trends, and brands are analyzed. Fruit flavors were m…
Portrayal of electronic cigarettes on YouTube
The vast majority of information on YouTube about e-cigarettes promoted their use and depicted the use of e-cigarettes as socially acceptable. It is critical to develop appropriate health campaigns to inform e-cigarette consumers of potential harms associated with e-cigarette use
Infectious Disease Informatics
Intelligence and Security Informatics
Equitable access to Covid-19 vaccines makes a life-saving difference to all countries
Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HIC…
Intelligence and Security Informatics
Infectious Disease Informatics
Portrayal of electronic cigarettes on YouTube
The vast majority of information on YouTube about e-cigarettes promoted their use and depicted the use of e-cigarettes as socially acceptable. It is critical to develop appropriate health campaigns to inform e-cigarette consumers of potential harms associated with e-cigarette use
An Examination of Electronic Cigarette Content on Social Media
In recent years, the emerging electronic cigarette (e-cigarette) marketplace has shown great development prospects all over the world. Reddit, one of the most popular forums in the world, has a very large user group and thus great influence. This study aims to gain a systematic understanding of e-cigarette flavors based on data collected from Reddit. Flavor popularity, mixing, characteristics, trends, and brands are analyzed. Fruit flavors were m…
Analysis of symptoms and their potential associations with e-liquids’ components
E-liquid components shown to be associated with e-cigarette use symptomology were VG/PG ratios, flavors, and nicotine levels. Future analysis could be conducted based on the structure of e-liquid components categories built in this study. Information revealed in this study could be utilized by e-cigarette users to understand the relationship between e-liquid type and symptoms experienced, by vendors to choose appropriate recipes of e-liquid, and …
Mining online e-liquid reviews for opinion polarities about e-liquid features
We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users
Aggregate effects of advertising decisions
Purpose The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search…
Game Starts at GameStop
In January 2021, the users of subreddit r/wallstreetbets (WSB) triggered an unprecedented short squeeze by driving up GameStop’s stock price to an unimaginable high point. During the event, a large number of users participated in the discussion about GameStop and coordinated trading behavior on r/WSB to push the stock price higher. In this article, we investigate the characteristics of the collective behaviors and social dynamics from the evoluti…
Equitable access to Covid-19 vaccines makes a life-saving difference to all countries
Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HIC…
Towards Human–Machine Recognition Alignment
The multimodality nature of web data has necessitated complex multimodal information retrieval for a wide range of web applications. Deep neural networks (DNNs) have been widely employed to extract semantic features from raw samples to improve retrieval accuracy. In addition, hashing is widely used to improve computational and storage efficiency. As such, deep hashing frameworks have been applied for multimodal retrieval tasks. However, there is …
A Deep Learning Approach for Semantic Analysis of Covid-19-Related Stigma on Social Media
The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the earl…
Hashing Fake
The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semanti…
CGNN
With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous …
Graph Representation Learning of Multilayer Spatial–Temporal Networks for Stock Predictions
Accurate stock market prediction is crucial for investors seeking significant profits. With increased economic activity, various interrelations between listed companies have become important for accurate predictions. These relations can be represented as complex financial networks, aiding the development of effective graph neural network (GNN) prediction methods. However, current GNN-based methods for stock prediction typically rely on a single s…
Social Cognition-Enhanced Public Opinion Response During Emergencies
Computer Science (12 works) · Medicine (8 works) · World Wide Web (7 works) · Artificial Intelligence (6 works) · Psychology (5 works) · Social media (5 works) · Advertising (4 works) · Business (4 works) · Public health (4 works) · Biostatistics (3 works)