Related papers: How do academic topics shift across altmetric sources? A case study of the research area of Big Data

How do academic topics shift across altmetric sources? A case study of the research area of Big Data

URL: http://arxiv.org/abs/2003.10508v1
Date: Mon, 23 Mar 2020 19:37:36 GMT
Title: How do academic topics shift across altmetric sources? A case study of the research area of Big Data
Authors: Xiaozan Lyu and Rodrigo Costas
Abstract summary: Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric. Results show there are substantial differences between the two sets of topics around Big Data scientific research. Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research.
Score: 2.208242292882514
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Taking the research area of Big Data as a case study, we propose an approach for exploring how academic topics shift through the interactions among audiences across different altmetric sources. Data used is obtained from Web of Science (WoS) and Altmetric.com, with a focus on Blog, News, Policy, Wikipedia, and Twitter. Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric. Different measures are applied to determine the (dis)similarities between the topics put forward by the publication authors and those by the online audiences. Results show that overall there are substantial differences between the two sets of topics around Big Data scientific research. The main exception is Twitter, where high-frequency hashtags in tweets have a stronger concordance with the author keywords in publications. Among the online communities, Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research. Specifically, the audiences not only focus on more easy-to-understand academic topics related to social or general issues, but also extend them to a broader range of topics in their online discussions. This study lays the foundations for further investigations about the role of online audiences in the transformation of academic topics across altmetric sources, and the degree of concern and reception of scholarly contents by online communities.

Related papers

Amplifying Academic Research through YouTube: Engagement Metrics as Predictors of Citation Impact [0.0]
This study explores the interplay between YouTube engagement metrics and the academic impact of cited publications within video descriptions. By analyzing data from Altmetric.com and YouTube's API, it assesses how YouTube video features relate to citation impact.
arXiv Detail & Related papers (2024-05-21T12:43:37Z)
Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
We investigate the role of social media influencers in enhancing the visibility of machine learning research. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
arXiv Detail & Related papers (2024-01-24T20:05:49Z)
Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments [1.7230140898679147]
This paper investigates sentiment and semantic relationships among comments across various social media platforms. It uses word embeddings to analyze components in sentences and documents. Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large interconnected brain.
arXiv Detail & Related papers (2023-09-15T04:57:23Z)
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines [63.52264764099532]
We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022. We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
arXiv Detail & Related papers (2023-03-28T03:31:37Z)
Quantifying the Online Long-Term Interest in Research [0.0]
Being cognizant of how long a research article is mentioned online could be valuable information to the researchers. We analyzed multiple social media platforms on which users share and/or discuss scholarly articles. Using the online social media metrics for each of these three clusters, we built machine learning models to predict the long-term online interest in research articles.
arXiv Detail & Related papers (2022-09-13T16:57:44Z)
Tag-Aware Document Representation for Research Paper Recommendation [68.8204255655161]
We propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users. The proposed model is effective in recommending research papers even when the rating data is very sparse.
arXiv Detail & Related papers (2022-09-08T09:13:07Z)
Information Retention in the Multi-platform Sharing of Science [1.4626565477022566]
We examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles. We find a strong tendency towards low levels of information retention, following a distinct trajectory of loss. sequences involving more platforms tend to be associated with higher information retention.
arXiv Detail & Related papers (2022-07-27T22:28:48Z)
Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research [2.208242292882514]
We study the academic age, production, and research focus of the community of authors active in Big Data research. Results show that the academic realm of "Big Data" is a growing topic with an expanding community of authors.
arXiv Detail & Related papers (2021-06-10T08:17:09Z)
The Influence of Domain-Based Preprocessing on Subject-Specific Clustering [55.41644538483948]
The sudden change of moving the majority of teaching online at Universities has caused an increased amount of workload for academics. One way to deal with this problem is to cluster these questions depending on their topic. In this paper, we explore the realms of tagging data sets, focusing on identifying code excerpts and providing empirical results.
arXiv Detail & Related papers (2020-11-16T17:47:19Z)
Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation [66.99734491847076]
We propose a Chinese multi-domain knowledge-driven conversation dataset, KdConv, which grounds the topics in multi-turn conversations to knowledge graphs. Our corpus contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0.
arXiv Detail & Related papers (2020-04-08T16:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.