The evolution of IJHCS and CHI: A quantitative analysis

Mannocci, Andrea; Osborne, Francesco; Motta, Enrico

doi:10.1016/j.ijhcs.2019.05.009

Cited by 19 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. [57] identified an unequal global distribution of publications within both CHI and the International Journal of Human-Computer Studies. They concluded that "there are a number of countries that show a very high level of interest in what is happening in IJHCS and HCI but are unable to have a significant publishing presence or citation impact in these outlets."…”

Section: Related Workmentioning

confidence: 99%

“…A factor contributing to this problem is that researchers in HCI are predominantly located in Western countries [5,57], which suggests that the majority of samples likely consist of Westerners. That the lack of geographic diversity in both authors and participants of published articles is a problem has been widely recognized in the behavioral sciences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How WEIRD is CHI?

Linxen¹,

Sturm²,

Brühlmann³

et al. 2021

Preprint

View full text Add to dashboard Cite

Computer technology is often designed in technology hubs in Western countries, invariably making it “WEIRD”, because it is based on the intuition, knowledge, and values of people who are Western, Educated, Industrialized, Rich, and Democratic. Developing technology that is universally useful and engaging requires knowledge about members of WEIRD and non-WEIRD societies alike. In other words, it requires us, the CHI community, to generate this knowledge by studying representative participant samples. To find out to what extent CHI participant samples are from Western societies, we analyzed papers published in the CHI proceedings between 2016-2020. Our findings show that 73% of CHI study findings are based on Western participant samples, representing less than 12% of the world’s population. Furthermore, we show that most participant samples at CHI tend to come from industrialized, rich, and democratic countries with generally highly educated populations. Encouragingly, recent years have seen a slight increase in non-Western samples and those that include several countries. We discuss suggestions for further broadening the international representation of CHI participant samples.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How WEIRD is CHI?

Linxen¹,

Sturm²,

Brühlmann³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The human experts were given this spreadsheet and asked to classify each paper either with one of the five categories or with a "none of the above" tag. We then compared the seven annotation sets produced by the six human experts and by EDAM, considered as an additional annotator 29 . Table 2 shows the agreement between the annotators.…”

Section: Evaluation Of the Primary Study Classificationmentioning

confidence: 99%

Reducing the effort for systematic reviews in software engineering

Osborne

Muccini

Lago

et al. 2019

Self Cite

View full text Add to dashboard Cite

Context. Systematic Reviews (SRs) are means for collecting and synthesizing evidence from the identification and analysis of relevant studies from multiple sources. To this aim, they use a well-defined methodology meant to mitigate the risks of biases and ensure repeatability for later updates. SRs, however, involve significant effort. Goal. The goal of this paper is to introduce a novel methodology that reduces the amount of manual tedious tasks involved in SRs while taking advantage of the value provided by human expertise. Method. Starting from current methodologies for SRs, we replaced the steps of keywording and data extraction with an automatic methodology for generating a domain ontology and classifying the primary studies. This methodology has been applied in the Software Engineering sub-area of Software Architecture and evaluated by human annotators. Results. The result is a novel Expert-Driven Automatic Methodology, EDAM, for assisting researchers in performing SRs. EDAM combines ontology-learning techniques and semantic technologies with the human-in-the-loop. The first (thanks to automation) fosters scalability, objectivity, reproducibility and granularity of the studies; the second allows tailoring to the specific focus of the study at hand and knowledge reuse from domain experts. We evaluated EDAM on the field of Software Architecture against six senior researchers. As a result, we found that the performance of the senior researchers in classifying papers was not statistically significantly different from EDAM. Conclusions. Thanks to automation of the less-creative steps in SRs, our methodology allows researchers to skip the tedious tasks of keywording and manually classifying primary studies, thus freeing effort for the analysis and the discussion.

show abstract

“…Furthermore, AIDA can be used to train machine learning systems for predicting the impact of research dynamics [11]. A preliminary versions of AIDA was used to support a comprehensive analysis of the research trends in the main venues of Human-Computer Interaction [9].…”

Section: Introductionmentioning

confidence: 99%

Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Angioni

Salatino

Osborne

et al. 2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

Self Cite

View full text Add to dashboard Cite

Academia and industry are constantly engaged in a joint effort for producing scientific knowledge that will shape the society of the future. Analysing the knowledge flow between them and understanding how they influence each other is a critical task for researchers, governments, funding bodies, investors, and companies. However, current corpora are unfit to support large-scale analysis of the knowledge flow between academia and industry since they lack of a good characterization of research topics and industrial sectors. In this short paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterizes 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author's affiliations (academy, industry, or collaborative) and 66 industrial sectors (e.g., automotive, financial, energy, electronics) obtained from DBpedia. AIDA was generated by an automatic pipeline that integrates several knowledge graphs and bibliographic corpora, including Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database.

show abstract

The evolution of IJHCS and CHI: A quantitative analysis

Cited by 19 publications

References 42 publications

How WEIRD is CHI?

How WEIRD is CHI?

Reducing the effort for systematic reviews in software engineering

Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Contact Info

Product

Resources

About