Yoo Kyung Jeong

Content-based author co-citation analysis

Author co-citation analysis (ACA) has long been used as an effective method for identifying the intellectual structure of a research domain, but it relies on simple co-citation counting, which does not take the citation content into consideration. The present study proposes a new method for measuring the similarity between co-cited authors by considering authors citation content. We collected the full-text journal articles in the information science domain and extracted the citing sentences to calculate their similarity distances. We compared our method with traditional ACA and found out that our approach, while displaying a similar intellectual structure for the information science domain as the other baseline methods, also provides more details about the sub-disciplines in the domain than with traditional ACA.

Analyzing the Political Landscape of 2012 Korean Presidential Election in Twitter

Social media is changing existing information behavior by giving users access to real-time online information channels without the constraints of time and space. Social media, therefore, has created an enormous data analysis challenge for scientists trying to keep pace with developments in their field. Most previous studies have adopted broad-brush approaches that typically result in limited analysis possibilities. To address this problem, we applied text-mining techniques to Twitter data related to the 2012 Korean presidential election. We use three primary techniques: topic modeling to track changes in topical trends, mention-direction-based user network analysis, and term co-occurrence retrieval for further content analysis. Our study reveals that Twitter could be a useful way to detect and trace the advent of and changes in social issues, while analyzing mention-based user networks could show different aspects of user behaviors.

Topic-Network based Topic Shift Detection on Twitter

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public`s negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

Content- and proximity-based author co-citation analysis using citation sentences

Author co-citation analysis (ACA) has been widely used for identifying the subject disciplines of authors. Citations can reveal the explicit relationship between authors as well as their subject research fields. However, previous studies have seldom considered citation contents that convey useful implicit information on the authors or the influence of the links between the authors’ subject fields by taking citation locations into account. This study aims to reveal the implicit relationship in the authors’ subject disciplines by considering both citation contents and proximity. To this end, the researchers propose a new ACA method, called content- and proximity-based author co-citation analysis (CPACA). For the study, we extracted citation sentences and locations from full-text articles in the oncology field. The top 15 journals on oncology in Journal Citation Reports were selected, and 6,360 full-text articles from PubMed Central were collected. The results show that the proposed method enables the identification of distinct sub-fields of authors to represent authors’ subject relatedness.

Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news

The present study investigates topic coverage and sentiment dynamics of two different media sources, Twitter and news publications, on the hot health issue of Ebola. We conduct content and sentiment analysis by: (1) applying vocabulary control to collected datasets; (2) employing the n-gram LDA topic modeling technique; (3) adopting entity extraction and entity network; and (4) introducing the concept of topic-based sentiment scores. With the query term ‘Ebola’ or ‘Ebola virus’, we collected 16,189 news articles from 1006 different publications and 7,106,297 tweets with the Twitter stream API. The experiments indicate that topic coverage of Twitter is narrower and more blurry than that of the news media. In terms of sentiment dynamics, the life span and variance of sentiment on Twitter is shorter and smaller than in the news. In addition, we observe that news articles focus more on event-related entities such as person, organization and location, whereas Twitter covers more time-oriented entities. Based on the results, we report on the characteristics of Twitter and news media as two distinct news outlets in terms of content coverage and sentiment dynamics.

Trajectory analysis of drug-research trends in pancreatic cancer on PubMed and ClinicalTrials.gov

Increasing interest in developing treatments for pancreatic cancer has led to a surge in publications in the field. Analyses of drug-research trends are needed to minimize risk in anti-cancer drug development. Here, we analyzed publications on anti-cancer drugs extracted from PubMed records and ClinicalTrials datasets. We conducted a drug cluster analysis by proposing the entity Dirichlet Multinomial Regression (eDMR) technique and in-depth network analysis of drug cluster and target proteins. The results show two distinct research clusters in both the ClinicalTrials dataset and the PubMed records. Specifically, various targets associated with anti-cancer drugs are investigated in new drug testing while the diverse chemicals are studied together with a standard therapeutic agent in the academic literature. In addition, our study confirms that drug research published in PubMed is preceded by clinical trials. Although we only evaluate drugs for pancreatic cancer in the present study, our method can be applied to drug-research trends of other diseases.

Investigating the integrated landscape of the intellectual topology of bioinformatics

We aim at identifying (1) whether and how various data sources influence mapping an intellectual structure of the field of bioinformatics, and (2) the landscape of bioinformatics by integrating those sources. To this end, we conduct a comprehensive bibliometric analysis by harvesting bibliographic information from DBLP, PubMed Central, and Web of Science. We then measure and compare topological characteristics of networks generated using these sources. The results show a dichotomous pattern dominated by PubMed Central and WoS. In addition, a few influential scientists in the field of bioinformatics receive very high citations from their colleagues, which is a driving force to bloom the field. These few scientists are connected to a much larger research community. Most of the researchers are intellectually linked within a few steps, in spite of the domain’s interdisciplinary characteristics. Particularly, influential authors consist of a small world. We also identify that there is not a coherent body of discipline in bioinformatics since the field is still under development. Finally, the journals and conferences indexed by each source cover different research topics, and PubMed Central is more inclusive than DBLP as an indexing database.

Docosahexaenoic Acid Inhibits Cerulein-Induced Acute Pancreatitis in Rats

Oxidative stress is an important regulator in the pathogenesis of acute pancreatitis (AP). Reactive oxygen species induce activation of inflammatory cascades, inflammatory cell recruitment, and tissue damage. NF-κB regulates inflammatory cytokine gene expression, which induces an acute, edematous form of pancreatitis. Protein kinase C δ (PKCδ) activates NF-κB as shown in a mouse model of cerulein-induced AP. Docosahexaenoic acid (DHA), an ω-3 fatty acid, exerts anti-inflammatory and antioxidant effects in various cells and tissues. This study investigated whether DHA inhibits cerulein-induced AP in rats by assessing pancreatic edema, myeloperoxidase activity, levels of lipid peroxide and IL-6, activation of NF-κB and PKCδ, and by histologic observation. AP was induced by intraperitoneal injection (i.p.) of cerulein (50 μg/kg) every hour for 7 h. DHA (13 mg/kg) was administered i.p. for three days before AP induction. Pretreatment with DHA reduced cerulein-induced activation of NF-κB, PKCδ, and IL-6 in pancreatic tissues of rats. DHA suppressed pancreatic edema and decreased the abundance of lipid peroxide, myeloperoxidase activity, and inflammatory cell infiltration into the pancreatic tissues of cerulein-stimulated rats. Therefore, DHA may help prevent the development of pancreatitis by suppressing the activation of NF-κB and PKCδ, expression of IL-6, and oxidative damage to the pancreas.

Identifying the topology of the K-pop video community on YouTube: A combined Co-comment analysis approach

YouTube is a successful social network that people use to upload, watch, and comment on videos. We believe comments left on these videos can provide insight into user interests, but to this point have not been used to map out a specific video community. Our study investigates whether and how user commenting behavior impacts the topology of the K‐pop video community through analysis of co‐commenting behavior on these videos. We apply a traditional author cocitation analysis to this behavior, in a process we refer to as co‐comment analysis, to detect the topology of this community. This involves: a) an analysis of user co‐comments to elicit the inclination of user homophily within the community; b) an analysis of user co‐comments, weighted frequency of co‐comments, to detect user interests in the community; and c) an analysis of user co‐comments, weighted sentiment scores, to capture user opinions by polarity. The results indicate that users who comment on specific K‐pop videos also tend to comment on topically similar YouTube videos. We also find that the number of comments made by users correlates with the degree of positivity of their comments. Conversely, users who comment negatively on K‐pop videos are not inclined to form specific user groups, but rather present only their opinions individually.

A Mini-Review on the Effect of Docosahexaenoic Acid (DHA) on Cerulein-Induced and Hypertriglyceridemic Acute Pancreatitis

Acute pancreatitis refers to the sudden inflammation of the pancreas. It is associated with premature activation and release of digestive enzymes into the pancreatic interstitium and systemic circulation, resulting in pancreatic tissue autodigestion and multiple organ dysfunction, as well as with increased cytokine production, ultimately leading to deleterious local and systemic effects. Although mechanisms involved in pathogenesis of acute pancreatitis have not been completely elucidated, oxidative stress is regarded as a major risk factor. In human acute pancreatitis, lipid peroxide levels in pancreatic tissues increase. Docosahexaenoic acid (DHA), an omega-3 polyunsaturated fatty acid (C22:6n-3), exerts anti-inflammatory and antioxidant effects on various cells. Previous studies have shown that DHA activates peroxisome proliferator-activated receptor-γ and induces catalase, which inhibits oxidative stress-mediated inflammatory signaling required for cytokine expression in experimental acute pancreatitis using cerulein. Cerulein, a cholecystokinin analog, induces intra-acinar activation of trypsinogen in the pancreas, which results in human acute pancreatitis-like symptoms. Therefore, DHA supplementation may be beneficial for preventing or inhibiting acute pancreatitis development. Since DHA reduces serum triglyceride levels, addition of DHA to lipid-lowering drugs like statins has been investigated to reduce hypertriglyceridemic acute pancreatitis. However, high DHA concentrations increase cytosolic Ca2+, which activates protein kinase C and may induce hyperlipidemic acute pancreatitis. In this review, effect of DHA on cerulein-induced and hypertriglyceridemic acute pancreatitis has been discussed. The relation of high concentration of DHA to hyperlipidemic acute pancreatitis has been included.