301 research outputs found
Exploring Features for Predicting Policy Citations
In this study we performed an initial investigation and evaluation of
altmetrics and their relationship with public policy citation of research
papers. We examined methods for using altmetrics and other data to predict
whether a research paper is cited in public policy and applied receiver
operating characteristic curve on various feature groups in order to evaluate
their potential usefulness. From the methods we tested, classifying based on
tweet count provided the best results, achieving an area under the ROC curve of
0.91.Comment: 2 pages, accepted to JCDL '1
Does society show differential attention to researchers based on gender and field?
While not all researchers prioritize social impact, it is undeniably a
crucial aspect that adds significance to their work. The objective of this
paper is to explore potential gender differences in the social attention paid
to researchers and to examine their association with specific fields of study.
To achieve this goal, the paper analyzes four dimensions of social influence
and examines three measures of social attention to researchers. The dimensions
are media influence (mentions in mainstream news), political influence
(mentions in public policy reports), social media influence (mentions in
Twitter), and educational influence (mentions in Wikipedia). The measures of
social attention to researchers are: proportion of publications with social
mentions (social attention orientation), mentions per publication (level of
social attention), and mentions per mentioned publication (intensity of social
attention). By analyzing the rankings of authors -- for the four dimensions
with the three measures in the 22 research fields of the Web of Science
database -- and by using Spearman correlation coefficients, we conclude that:
1) significant differences are observed between fields; 2) the dimensions
capture different and independent aspects of the social impact. Finally, we use
non-parametric means comparison tests to detect gender bias in social
attention. We conclude that for most fields and dimensions with enough non-zero
altmetrics data, gender differences in social attention are not predominant,
but are still present and vary across fields.Comment: 23 pages, 5 figures, 7 table
The Many Publics of Science: Using Altmetrics to Identify Common Communication Channels by Scientific field
Altmetrics have led to new quantitative studies of science through social
media interactions. However, there are no models of science communication that
respond to the multiplicity of non-academic channels. Using the 3653 authors
with the highest volume of altmetrics mentions from the main channels (Twitter,
News, Facebook, Wikipedia, Blog, Policy documents, and Peer reviews) to their
publications (2016-2020), it has been analyzed where the audiences of each
discipline are located. The results evidence the generalities and specificities
of these new communication models and the differences between areas. These
findings are useful for the development of science communication policies and
strategies
The Botization of Science? Large-scale study of the presence and impact of Twitter bots in science dissemination
Twitter bots are a controversial element of the platform, and their negative
impact is well known. In the field of scientific communication, they have been
perceived in a more positive light, and the accounts that serve as feeds
alerting about scientific publications are quite common. However, despite being
aware of the presence of bots in the dissemination of science, no large-scale
estimations have been made nor has it been evaluated if they can truly
interfere with altmetrics. Analyzing a dataset of 3,744,231 papers published
between 2017 and 2021 and their associated 51,230,936 Twitter mentions, our
goal was to determine the volume of publications mentioned by bots and whether
they skew altmetrics indicators. Using the BotometerLite API, we categorized
Twitter accounts based on their likelihood of being bots. The results showed
that 11,073 accounts (0.23% of total users) exhibited automated behavior,
contributing to 4.72% of all mentions. A significant bias was observed in the
activity of bots. Their presence was particularly pronounced in disciplines
such as Mathematics, Physics, and Space Sciences, with some specialties even
exceeding 70% of the tweets. However, these are extreme cases, and the impact
of this activity on altmetrics varies by speciality, with minimal influence in
Arts & Humanities and Social Sciences. This research emphasizes the importance
of distinguishing between specialties and disciplines when using Twitter as an
altmetric
An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics
Sufficient data presence is one of the key preconditions for applying metrics
in practice. Based on both Altmetric.com data and Mendeley data collected up to
2019, this paper presents a state-of-the-art analysis of the presence of 12
kinds of altmetric events for nearly 12.3 million Web of Science publications
published between 2012 and 2018. Results show that even though an upward trend
of data presence can be observed over time, except for Mendeley readers and
Twitter mentions, the overall presence of most altmetric data is still low. The
majority of altmetric events go to publications in the fields of Biomedical and
Health Sciences, Social Sciences and Humanities, and Life and Earth Sciences.
As to research topics, the level of attention received by research topics
varies across altmetric data, and specific altmetric data show different
preferences for research topics, on the basis of which a framework for
identifying hot research topics is proposed and applied to detect research
topics with higher levels of attention garnered on certain altmetric data
source. Twitter mentions and policy document citations were selected as two
examples to identify hot research topics of interest of Twitter users and
policy-makers, respectively, shedding light on the potential of altmetric data
in monitoring research trends of specific social attention
Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy
In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points
OpenML: networked science in machine learning
Many sciences have made significant breakthroughs by adopting online tools
that help organize, structure and mine information that is too detailed to be
printed in journals. In this paper, we introduce OpenML, a place for machine
learning researchers to share and organize data in fine detail, so that they
can work more effectively, be more visible, and collaborate with others to
tackle harder problems. We discuss how OpenML relates to other examples of
networked science and what benefits it brings for machine learning research,
individual scientists, as well as students and practitioners.Comment: 12 pages, 10 figure
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