Journal of Data and Information Science ›› 2016, Vol. 1 ›› Issue (1): 28-49.doi: 10.20309/jdis.201604

• Research Paper • Previous Articles     Next Articles

Understanding the Correlations between Social Attention and Topic Trends of Scientific Publications

Xianlei Dong1, Jian Xu2, Ying Ding3, Chenwei Zhang3, Kunpeng Zhang4 & Min Song5   

  1. 1 School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China;
    2 School of Information Management, Sun Yat-sen University, Guangzhou 510006, China;
    3 Department of Information and Library Science, Indiana University, Bloomington, IN 47405, USA;
    4 Department of Information and Decision Sciences, University of Illinois at Chicago, IL 60607, USA;
    5 Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seoul 120-749, Republic of Korea
  • Received:2016-01-18 Revised:2016-02-26 Online:2016-03-15 Published:2016-03-15
  • Contact: Min Song
  • Supported by:
    This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012-2012S1A3A2033291) and by the Yonsei University Future-leading Research Initiative of 2014.

Abstract: Purpose: We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications.

Design/methodology/approach: First, topics are generated from the obesity corpus by using the latent Dirichlet allocation (LDA) algorithm and time series of keyword search trends in Google Trends are obtained. We then establish the structural time series model using data from January 2004 to December 2012, and evaluate the model using data from January 2013. We employ a state-space model to separate different non-regression components in an observational time series (i.e. the tendency and the seasonality) and apply the “spike and slab prior” and stepwise regression to analyze the correlations between the regression component and the social media attention. The two parts are combined using Markov-chain Monte Carlo sampling techniques to obtain our results.

Findings: The results of our study show that (1) the number of publications on child obesity increases at a lower rate than that of diabetes publications; (2) the number of publication on a given topic may exhibit a relationship with the season or time of year; and (3) there exists a correlation between the number of publications on a given topic and its social media attention, i.e. the search frequency related to that topic as identified by Google Trends. We found that our model is also able to predict the number of publications related to a given topic.

Research limitations: First, we study a correlation rather than causality between topics' trends and social media. As a result, the relationships might not be robust, so we cannot predict the future in the long run. Second, we cannot identify the reasons or conditions that are driving obesity topics to present such tendencies and seasonal patterns, so we might need to do “field” study in the future. Third, we need to improve the efficiency of our model by finding more efficient variable selection models, because the stepwise regression method is time consuming, especially for a large number of variables.

Practical implications: This paper analyzes publication topic trends from three perspectives: tendency, seasonality, and correlation with social media attention, providing a new perspective for identifying and understanding topical themes in academic publications.

Originality/value: To the best of our knowledge, we are the first to apply the state-space model to examine the relationships between healthcare-related publications and social media to investigate the relationships between a topic's evolvement and people's search behavior in social media. This paper thus provides a new viewpoint in the correlation analysis area, and demonstrates the value of considering social media attention in the analysis of publication topic trends.

Key words: Social media, Publication topic trends, Correlation, State-space model, Variable selection, Nowcasting