Research on pathways of expert finding on academic social networking sites

Highlights

• In the academic context, focused on the peer seeking behavior in pages, movements and pathways from experimental situation.

• Three types for pathways are identified by cluster analysis, combined with questionnaires and interviews.

• The search intensions have an impact on the different pathways.

• Implications for the interface design of ASNS are proposed based on the characteristics of searching process.

Abstract

The advent of academic social networking sites (ASNS) has provided a more convenient way for users to seek out experts. This paper uses ResearchGate to explore the features of pathways through four different tasks, combined with questionnaires and interviews. These tasks are aimed at identifying the right experts with relevant expertise or experience for a given topic. We propose a pathway-based approach that takes into consideration both pages and navigations. The pathways show the whole process of how users find experts on ASNS for a variety of reasons. A user visiting ASNS will generate different types of pages, and navigation represents the act of moving from one page to another. A series of navigations between pages that can be linked together chronologically make up the pathways. A total of 94 valid pathways were used for the cluster analysis. The results show that the pages most frequently accessed by users are profile pages, search pages, and publication pages. A comparison of these three types found that the overall utilization of publication pages is low. Users pay more attention to research results pages within profile pages, and continuously adjust the queries on demand. Single seeking pathways, triangular seeking pathways and multiple seeking pathways are identified to understand the process of users’ expert finding. Finally, this paper proposes implications for the interface design of ASNS: improve search pages, focus on users' personalized needs, and apply relational networks in the seeking process. Furthermore, the findings have a positive impact on encouraging interaction, collaboration and innovation in the research environment.

Introduction

With the rapid development of technology, academic social networking sites (ASNS) have received widespread attention, and have allowed for online cooperation and communication with experts. Some scholars define ASNS by focusing on the network of relationships between users. Kong, Shi, Yu, Liu, & Xia, 2019 define ASNS as complex heterogeneous networks formed by a large number of academic entities (publications, authors, etc.) and relationships (co-authors, citations, co-citations, etc.). Other scholars believe that ASNS provide support for academic activities in addition to constructing virtual academic networks. Elsayed (2016) found that ASNS can provide researchers with a variety of academic activities, one of which is the sharing and exchange of academic expertise. ASNS can also help scholars build professional networks, promote research activities (Jeng, He, & Jiang, 2015) and provide opportunities to publish articles and connect with each other (Thelwall & Kousha, 2014). The rise of such networks has promoted new forms of interaction among individuals in the field of academic communication. Currently, some of the most widely-used ASNS are ResearchGate, Mendeley, and Academia.edu.

Searching for an expert involves finding the right person with the appropriate knowledge or skills (Li, Ma, Yang, & Wang, 2013). In an expert search task, the user's need is to identify those who have relevant expertise on a topic of interest (Macdonald & Ounis, 2006). Previous studies have shown that users often search for experts in social networks (Li et al., 2020; Smirnova & Balog, 2011; Zhang, Tang, & Li, 2007). Users commonly seek information about experts, and they are also interested in discovering relationships between them (Li et al., 2013). Users also tend to find authoritative experts and interesting users through community question answering (CQA) services. Therefore, expert finding has been one of the most interesting topics for researchers in information retrieval (Kardan, Omidvar, & Farahmandnia, 2011), especially on Q&A websites (Liu, Chen, Kao, & Wang, 2013) and within academic contexts (Cifariello, Ferragina, & Ponza, 2019). Massive efforts have been undertaken to improve the accuracy of expert finding in social networks. Most existing methods for expert finding can be classified into two groups: authority-based methods and topic-based methods (Yuan, Zhang, Tang, Hall, & Cabotà, 2020). Moreover, compared with social networks, users can more easily access expert information on ASNS. There is a need to discover how users seek out corresponding experts on ASNS.

Expert finding, in particular, is an important motivation for users utilizing ASNS. Despite the fact that ASNS are becoming more common in academic communication, the question of how users choose the right experts remains to be resolved. Presently, research on user behavior on ASNS often focuses on user sharing, acquisition (Corvello, Genovese, & Verteramo, 2014; Kalb, Pirkkalainen, Pawlowski, & Schoop, 2011) and Q&A behavior (Deng, Tong, & Fu, 2018, Deng, Tong, Lin, Li, & Liu, 2019; Jeng, DesAutels, He, & Li, 2017). Recently, a new approach has been proposed to find high-impact interdisciplinary users based on friend discipline distribution on ASNS (Wu & Zhang, 2019). More and more scholars are paying attention to the process of expert finding on ASNS, which is a more professional and interactive network.

Scholars agree that these ASNS necessitate a novel approach to better understand users' needs. Proposing a new expert finding method is very important for many users, especially in the academic environment. The focus of this paper is to determine how users find appropriate experts for their objectives and within their interests, what information is used (including what the users look at, what they search for and what they select) and what steps are taken to do so. The analysis is based on three levels of the pages, navigations and pathways (Fig. 1). Page refers to a user's access of and interaction on websites. Navigation represents the act of moving from one page to another, and chaining all the navigations in chronological order becomes a pathway, which can show the page and its sequence of user interaction with the website. This pathway-based approach aims to develop a whole search process, which contains a variety of methods to find the required expert to advise users and provide implications for the interface design of ASNS.

First, when a user accesses expert information on ASNS, each page request leaves a footprint on the page. It is convenient for users to identify information by the types of pages, such as personal profile, institutions, and departments. For instance, ResearchGate, Mendeley and Academia.edu can easily help users search for information by different page type (Fig. 2). Users can click to access a series of pages on a certain website according to different academic purposes. Therefore, we need to identify the features of the pages that users most frequently visit during the process of expert finding. The first research question is as follows.

RQ1: What types of pages do users frequently visit during expert finding on ASNS?

Second, in the actual search environment on ASNS, users seek out expert information not only to obtain a single page's information, but also to combine different types of information to find an unfamiliar expert, such as academic activities, academic relationships, academically shared content, etc. Multiple sources of information help users choose the most suitable one. Navigation between pages is also an important part of the user's pathway. Therefore, the second research question is as follows.

RQ2: What are the features of users’ navigations on pages when seeking out expert information on ASNS?

Third, users engaging in the seeking process not only apply traditional search methods, like using keywords to locate relevant pages, but also conduct a large number of non-search activities, such as paying attention to institutional hierarchy, collaborative networks, or citation networks. A pathway is composed of all the navigations taking place during one visit and can provide a user's whole process, including different academic purposes, browsing and search history. Pathway analysis addresses the third research question.

RQ3: What are the types and characteristics of users’ pathways when seeking out expert information on ASNS?

In this paper, the different types of pathways can be evaluated to measure users’ interaction effectiveness and better their academic experience. Website operators can use this data to optimize site layouts and navigation to provide visitors with more efficient choices that align with their reasons for visiting the ASNS. This paper is structured as follows. The next section presents a literature review of information seeking behavior on ASNS and expert finding in social networks. In Section 3, we introduce the methodology, followed by the data collection and data analysis. Section 4 will discuss the results by focusing on the main findings. Finally, we conclude by summarizing both the contributions and limitations of this study.