Should we introduce a dislike button for academic articles?

Abstract

There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing “negative” citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation‐based scores, for instance, the h‐index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of “negative” citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated.

Time to vote: Temporal clustering of user activity on Stack Overflow

“…In our previous F I G U R E 1 0 The normalized aggregated dynamics for later votes: this time, arrow sizes are scaled so that the flow from every node is constant. Note the dominating tendency to stay inside the blue (short-term) and green (long-term) clusters and to transition from the orange (medium-term) to the green ones paper (Geras et al, 2020), we pointed out that negative votes are not so much a (negative) measure of quality.…”

“…Still, there are only 10 posts which have received 100 Down-Votes; hence in the further analysis, we will restrict ourselves to Up-Votes only. Notably, recent findings (Geras et al, 2020) suggest that Down-Votes do not change the dynamics of post score significantly anyway.…”

“…The majority of studies take a data mining or machine learning approach to answer a variety of questions about their users and their behavior (e.g., regarding the behavior of novice software engineers (Chatterjee et al, 2020), gender differences (May et al, 2019), improving answer rates by gamification (Papoutsoglou et al, 2020; Zhou et al, 2020), how knowledge is generated and shared (Tausczik & Huang, 2020)). Some papers are interested in the modeling of the structure of the Stack Exchange network (e.g., Moutidis & Williams, 2021) or mechanisms governing its dynamics (e.g., describing the effects of negative votes (Geras et al, 2020) or predicting the emergence of highly cited (Zhao et al, 2021) or obsolete (Zhang et al, 2021) posts).…”

Critical citations in knowledge construction and citation analysis: from paradox to definition

“…: PubPeer), or to an anonymous negative vote on an online site (e.g. : Stack Exchange, used by Geras et al (2020) to model the impact of a "dislike button" for publications). In the following section, we will endeavour to define what a critical citation is by trying to identify what causes this polarity shift.…”

“…A specific column indicates whether the label is part of a typology or not. Mixed opinion (Frost 1979;Lyu et al 2021) Yes X Negational citations (Bertin and Atanassova 2016;Cano 1989;Chubin and Moitra 1975;Hassan and Serenko 2019;Li et al 2013;Lin 2018;MacRoberts and MacRoberts 1984;Moravcsik and Murugesan 1975;Swales 1986) Yes/ No X Negative citations (Ball 2015;Catalini et al 2015;Di Iorio et al 2018;Erikson and Erlandson 2014;Geras et al 2020;JHA et al 2017;Liu 2017;Schneider et al 2020;Shadish et al 1995;Small 2004;Teufel et al 2006a;White 2004;Yu 2014;Zhang et al 2013) Yes/No X Negative credit (Brooks 1985) Yes X…”