-
Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021)
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- Date
- 20. 1.2015 18:30:22
12. 3.2021 18:45:15
-
Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020)
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- Date
- 20. 1.2015 18:30:22
12. 3.2021 18:41:28
-
Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023)
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- Date
- 22. 6.2023 18:07:12
-
Kudlow, P.; Dziadyk, D.B.; Rutledge, A.; Shachak, A.; Eysenbach, G.: ¬The citation advantage of promoted articles in a cross-publisher distribution platform : a 12-month randomized controlled trial (2020)
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- Abstract
- There is currently a paucity of evidence-based strategies that have been shown to increase citations of peer-reviewed articles following their publication. We conducted a 12-month randomized controlled trial to examine whether the promotion of article links in an online cross-publisher distribution platform (TrendMD) affects citations. In all, 3,200 articles published in 64 peer-reviewed journals across eight subject areas were block randomized at the subject level to either the TrendMD group (n = 1,600) or the control group (n = 1,600) of the study. Our primary outcome compares the mean citations of articles randomized to TrendMD versus control after 12 months. Articles randomized to TrendMD showed a 50% increase in mean citations relative to control at 12 months. The difference in mean citations at 12 months for articles randomized to TrendMD versus control was 5.06, 95% confidence interval [2.87, 7.25], was statistically significant (p?<?.001) and found in three of eight subject areas. At 6 months following publication, articles randomized to TrendMD showed a smaller, yet statistically significant (p = .005), 21% increase in mean citations, relative to control. To our knowledge, this is the first randomized controlled trial to demonstrate how an intervention can be used to increase citations of peer-reviewed articles after they have been published.
- Date
- 12. 9.2020 20:40:33
-
Springer, M.: Erlahmt die Forschung? (2023)
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- Date
- 18. 2.2023 19:12:28
-
Reichmann, G.; Schlögl, C.: Möglichkeiten zur Steuerung der Ergebnisse einer Forschungsevaluation (2021)
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- Date
- 27.11.2013 12:50:12
-
Herb, U.; Geith, U.: Kriterien der qualitativen Bewertung wissenschaftlicher Publikationen : Befunde aus dem Projekt visOA (2020)
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- Date
- 27.11.2013 12:50:12
-
Manley, S.: Letters to the editor and the race for publication metrics (2022)
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- Date
- 6. 4.2022 19:22:26
-
Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023)
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- Date
- 21. 3.2023 19:22:14
-
Thelwall, M.; Foster, D.: Male or female gender-polarized YouTube videos are less viewed (2021)
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- Abstract
- As one of the world's most visited websites, YouTube is potentially influential for learning gendered attitudes. Nevertheless, despite evidence of gender influences within the site for some topics, the extent to which YouTube reflects or promotes male/female or other gender divides is unknown. This article analyses 10,211 YouTube videos published in 12 months from 2014 to 2015 using commenter-portrayed genders (inferred from usernames) and view counts from the end of 2019. Nonbinary genders are omitted for methodological reasons. Although there were highly male and female topics or themes (e.g., vehicles or beauty) and male or female gendering is the norm, videos with topics attracting both males and females tended to have more viewers (after approximately 5 years) than videos in male or female gendered topics. Similarly, within each topic, videos with gender balanced sets of commenters tend to attract more viewers. Thus, YouTube does not seem to be driving male-female gender differences.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1528-1544
-
Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023)
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- Date
- 22. 1.2023 18:37:33
-
Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023)
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- Date
- 21. 3.2023 19:22:02
-
Asubiaro, T.V.; Onaolapo, S.: ¬A comparative study of the coverage of African journals in Web of Science, Scopus, and CrossRef (2023)
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- Date
- 22. 6.2023 14:09:06
-
Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023)
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- Date
- 22. 6.2023 18:11:50
-
Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023)
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- Date
- 22. 6.2023 18:15:06
-
Liu, X.; Chen, X.: Authors' noninstitutional emails and their correlation with retraction (2021)
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- Date
- 9. 3.2021 12:33:02
-
Ma, L.: ¬The steering effects of citations and metrics (2021)
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- Date
- 12. 3.2021 18:15:12
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Cabanac, G.; Labbé, C.: Prevalence of nonsensical algorithmically generated papers in the scientific literature (2021)
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- Abstract
- In 2014 leading publishers withdrew more than 120 nonsensical publications automatically generated with the SCIgen program. Casual observations suggested that similar problematic papers are still published and sold, without follow-up retractions. No systematic screening has been performed and the prevalence of such nonsensical publications in the scientific literature is unknown. Our contribution is 2-fold. First, we designed a detector that combs the scientific literature for grammar-based computer-generated papers. Applied to SCIgen, it has a 83.6% precision. Second, we performed a scientometric study of the 243 detected SCIgen-papers from 19 publishers. We estimate the prevalence of SCIgen-papers to be 75 per million papers in Information and Computing Sciences. Only 19% of the 243 problematic papers were dealt with: formal retraction (12) or silent removal (34). Publishers still serve and sometimes sell the remaining 197 papers without any caveat. We found evidence of citation manipulation via edited SCIgen bibliographies. This work reveals metric gaming up to the point of absurdity: fraudsters publish nonsensical algorithmically generated papers featuring genuine references. It stresses the need to screen papers for nonsense before peer-review and chase citation manipulation in published papers. Overall, this is yet another illustration of the harmful effects of the pressure to publish or perish.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1461-1476
-
Tay, W.; Zhang, X.; Karimi , S.: Beyond mean rating : probabilistic aggregation of star ratings based on helpfulness (2020)
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- Date
- 11. 7.2020 12:55:58
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Fang, Z.; Dudek, J.; Costas, R.: Facing the volatility of tweets in altmetric research (2022)
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- Date
- 12. 7.2022 13:52:08
