Power Laws in altmetrics: An empirical analysis

Measuring altmetric events: the need for longer observation period and article level computations

Purpose

This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

Design/methodology/approach

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

Findings

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

Research limitations/implications

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

Practical implications

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

Social implications

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Originality/value

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.

Measuring serendipity with altmetrics and randomness

Many discussions on serendipitous research discovery stress its unfortunate immeasurability. This unobservability may be due to paradoxes that arise out of the usual conceptualizations of serendipity, such as “accidental” versus “goal-oriented” discovery, or “useful” versus “useless” finds. Departing from a different distinction drawn from information theory—bibliometric redundancy and bibliometric variety—this paper argues otherwise: Serendipity is measurable, namely with the help of altmetrics, but only if the condition of highest bibliometric variety, or randomness, obtains. Randomness means that the publication is recommended without any biases of citation counts, journal impact, publication year, author reputation, semantic proximity, etc. Thus, serendipity must be at play in a measurable way if a paper is recommended randomly, and if users react to that recommendation (observable via altmetrics). A possible design for a serendipity-measuring device would be a Twitter bot that regularly recommends a random scientific publication from a huge corpus to capture the user interactions via altmetrics. Other than its implications for the concept of serendipity, this paper also contributes to a better understanding of altmetrics’ use cases: not only do altmetrics serve the measurement of impact, the facilitation of impact, and the facilitation of serendipity, but also the measurement of serendipity.