Context Based Retrieval of Scientific Publications via Reader Lens

Mahalakshmi, G. S.; Siva, R.; Sendhilkumar, S.

doi:10.1007/978-81-322-2202-6_53

Cited by 4 publications

(9 citation statements)

References 19 publications

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“…The positivity results from either expressing positive opinions or reporting positive results, which can be itself considered a kind of confirmation, though not so explicit (Jia 2018). The finding is consistent with previous studies confirming the dominance of positive citations, which is attributed to authors' avoidance of directly criticizing or rejecting previous papers (MacRoberts and MacRoberts 1984;Athar and Teufel 2012;Mahalakshmi et al 2015;Sendhilkumar et al 2013). This may also be attributed to confirmation biases pushing authors to selectively cite those papers confirming their ideas, findings or hypotheses (Chubin and Moitra 1975;Leung et al 2017).…”

Section: Discussionsupporting

confidence: 92%

“…This may also be attributed to confirmation biases pushing authors to selectively cite those papers confirming their ideas, findings or hypotheses (Chubin and Moitra 1975;Leung et al 2017). In addition, there is a wide-spread tendency among authors and journals to report and accept positive results (MacRoberts and Mac-Roberts 1984;Athar and Teufel 2012;Matosin et al 2014;Mahalakshmi et al 2015).…”

Section: Discussionmentioning

confidence: 99%

“…To calculate co-citance textual similarity, all words, except for stop words, were analyzed. However, opinion analyses were performed on nouns, adjectives, adverbs, and verbs that are believed to carry opinion loads in a sentence (Esuli and Sebastiani 2007;Mahalakshmi et al 2015).…”

Section: Methodsmentioning

confidence: 99%

“…In this way, qualitative features can be added to citation analysis which may identify citation polarities and purposes (Abu-Jbara et al 2013). Although citances often consist of positive (Mahalakshmi et al 2015), rather than negative citation sentences (Athar 2014), ignoring the latter may result in losing sentiments and criticisms about the cited papers (Athar and Teufel 2012).…”

Section: Citance Opinion Miningmentioning

confidence: 99%

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In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

et al. 2019

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Document relational network has been effective in retrieving and evaluating papers. Despite their effectiveness, relational measures, including co-citation, are far from ideal and need improvements. The assumption underlying the co-citation relation is the content relevance and opinion relatedness of cited and citing papers. This may imply existence of some kind of co-opinionatedness between co-cited papers which may be effective in improving the measure. Therefore, the present study tries to test the existence of this phenomenon and its role in improving information retrieval. To do so, based on CITREC, a medical test collection was developed consisting of 30 queries (seed documents) and 4823 of their co-cited papers. Using NLP techniques, the co-citances of the queries and their co-cited papers were analyzed and their similarities were computed by 4 g similarity measure. Opinion scores were extracted from co-citances using SentiWordnet. Also, nDCG values were calculated and then compared in terms of the citation proximity index (CPI) and co-citedness measures before and after being normalized by the co-opinionatedness measure. The reliability of the test collection was measured by generalizability theory. The findings suggested that a majority of the co-citations exhibited a high level of co-opinionatedness in that they were mostly similar either in their opinion strengths or in their polarities. Although anti-polar co-citations were not trivial in their number, a significantly higher number of the co-citations were co-polar, with a majority being positive. The evaluation of the normalization of the CPI and co-citedness by the co-opinionatedness indicated a generally significant improvement in retrieval effectiveness. While anti-polar similarity reduced the effectiveness of the measure, the co-polar similarity proved to be effective in improving the co-citedness. Consequently, the co-opinionatedness can be presented as a new document relation and used as a normalization factor to improve retrieval performance and research evaluation.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Citance Opinion Miningmentioning

confidence: 99%

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In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

et al. 2019

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“…It has been found effective in improving IR (Yaghtin et al ., 2019a, b). The measure is theoretically based on our existing knowledge confirming the subject relatedness of co-citations (Small, 1973; Bichteler and Eaton, 1980), as well as the predominant tendency of citers to give confirmatory citations and avoid negative credits (MacRoberts and MacRoberts, 1984; Athar and Teufel, 2012; Mahalakshmi et al , 2015; Sendhilkumar et al , 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the co-citation dependence on semantic layers of co-cited documents

Yaghtin

Sotudeh

Nikseresht

et al. 2021

OIR

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PurposeCo-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.Design/methodology/approachThe present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.FindingsThe dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.Originality/valueIt is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

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Gender differences in citation sentiment: A case study in life sciences and biomedicine

Dehdarirad

Yaghtin

2022

Journal of Information Science

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In this study, we investigated whether female and male authors in the field of life sciences and biomedicine differed in their tendency for citation and citation sentiment. The data comprised two sets, cited set and citing set. Cited set comprised 17,237 articles whereas citing set comprised 115,935 articles. The cited set which is from the area Life Sciences & Biomedicine and published during 2012–2016 was retrieved from the Web of Science Medline. The citing set and its citation contexts were retrieved using the Colil database. The analysis was done using a combination of homophily analysis, regression analysis and a chi-square test. The covariates in the regression analyses were features related to authors, journal, institution, country and abstract readability. The homophily analysis showed a significant tendency for female (8%) and male (14%) authorship teams to cite papers by the same gender composition teams. In addition, the results of regression analysis (Model 1) and pairwise comparisons showed that male-authored papers received a significant higher positive sentiment compared with female-authored papers. The results of regression analysis (Model 2) showed a small significant positive association between gender similarity of cited and citing authorship teams and the sentiment score. However, further analysis using the chi-square test showed a significant lower tendency for women to use positive terms when citing the research findings of papers with the same gender composition. Men, in contrast, used significantly more positive terms when citing papers with the same gender composition. Finally, lay summary for a cited paper, country similarity and the venue of cited publication when it was a mega journal had a positive significant association with the sentiment score received.

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Context Based Retrieval of Scientific Publications via Reader Lens

Cited by 4 publications

References 19 publications

In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

Modeling the co-citation dependence on semantic layers of co-cited documents

Gender differences in citation sentiment: A case study in life sciences and biomedicine

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