The many faces of authors in academic social networking sites: looking for differences between Google Scholar Citations, Mendeley, and Microsoft Academic Search

Tsou, A., Bowman, T. D., Sugimoto, T., Lariviere, V., & Sugimoto, C.R.  
Self-presentation in scholarly profiles: Characteristics of images and perceptions of 
professionalism and attractiveness on academic social networking sites
First Monday, 2016, 21(4) 
doi:10.5210/fm.v21i4.6381

OBJECTIVES

In this paper we undertake an exploratory study to analyze how academics present themselves in profile pictures online and how they are perceived. In particular, we examine perceptions and predictors of professionalism, and how these are influenced by age, gender, and race. We examine differences amongst three different academic social networking platforms: Microsoft Academic Search, Mendeley, and Google Scholar. Furthermore, we analyze how framing individuals as “scholars” alters perceptions of professionalism. Specifically, we address the following research questions:

1. How do scholars present themselves online? Do presentation characteristics vary by demographic characteristic (i.e., gender, age, or race)? Do presentation characteristics vary by platform (i.e., Mendeley, Google Scholar, Microsoft Academic Search)?
2. What is the relationship between presentation characteristics and perceptions of professionalism? Does this vary by demographic characteristic (i.e., gender, age, race)?
3. What is the relationship between presentation characteristics and perceptions of attractiveness? Does this vary by demographic characteristic (i.e., gender, age, race)? How does priming affect the framing of perceptions of professionalism? How does priming affect the framing of perceptions of attractiveness?

METHODS

This study used Amazon’s Mechanical Turk service to code 10,500 profile pictures used by scholars on three platforms: Mendeley, Microsoft Academic Search, and Google Scholar. 
The data were gathered from three online social networking sites for academics: Microsoft Academic Search, Google Scholar, and Mendeley. Ultimately, 10,000 profile pictures were sampled from each of these sites, for a total initial sampling frame of 30,000 images. 
These images were coded by workers (known as Turkers) on Amazon’s Mechanical Turk (AMT) service. The HITs were titled “Image categorization.” Results were validated by a researcher who manually checked every image that had not been flagged as a photograph of a single adult. The images were coded for descriptive (e.g., age, race, gender), objective (e.g., presence/absence of glasses, color of clothes), and subjective (i.e., attractiveness, professionalism) variables. Each of the 10,500 images was posted as its own HIT on AMT, along with the codebook. In order to both validate the initial coding and investigate priming, another round of coding using the same 10,500 images was conducted using AMT Turkers. Two types of inter-rater reliability were conducted. For one round, a random sample of 100 images coded by AMT Turkers was compared against coding done by one of the researchers. The second round of coding used the priming coding to assess reliability amongst Turkers.

RESULTS

The majority of the individuals on Mendeley, Microsoft Academic Search, and Google Scholar were Caucasian, male, and perceived to be over the age of 35. Females and younger individuals were perceived as less professional than male and older individuals, while women were more likely to be perceived as “attractive.” In addition, the Mechanical Turk coders were susceptible to framing; the individuals in the profile pictures were considered more “professional” if they were identified as “scholars” rather than merely as “individuals.” The results have far-reaching implications for self-presentation and framing, both for scholars and for other professionals. In the academic realm, there are serious implications for hiring and the allocation of resources and rewards.

Regardless of the attention the results of this study may garner, to this blog (focused on sharing empirical evidences on Google Scholar), the most original aspect is the comparison of images available in Google Scholar Citations, Mendeley, and Microsoft Academic Seach profiles. These are the main results:

Several differences are found by social media platform. We noted that the proportion of profiles with images varies dramatically, with Mendeley having a much higher proportion than Google Scholar or Microsoft Academic Search, suggesting a difference in user behavior. Our initial analysis also revealed differences by gender, with Mendeley having the largest proportion of women and Google Scholar the lowest. Other significant differences revealed that Mendeley had the youngest users, as well as the ones most likely to be associated with non-professional variables — reinforcing earlier research on the demographics of Mendeley users (Haustein, et al., 2014). Google Scholar academics were perceived as being associated with more professional variables — e.g., wearing blue or black, wearing business and business casual, and employing a traditional head-and-shoulders shot. MAS users, who were the most likely to be older and depicted wearing glasses, were seen as the least attractive. 

These findings have several implications for sociological and scientific studies using social media data. For example, contemporary scientometric studies often use data from Mendeley or other sources to describe the impact of research (Haustein, et al., 2014). However, users of this platform are significantly different demographically from Google Scholar or MAS, in ways that can have implications for the results of such studies.

Stefanie Haustein, Vincent Larivière, Mike Thelwall, Didier Amyot, and Isabella Peters, 2014. “Tweets vs. Mendeley readers: How do these two social media metrics differ?” it-Information Technology, volume 56, number 5, pp. 207–215.

doi: http://dx.doi.org/10.1515/itit-2014-1048

Available
 http://www.ojphi.org/ojs/index.php/fm/article/view/6381/5343