Cited By
View all- Badache I(2020)2SRM: Learning social signals for predicting relevant search resultsWeb Intelligence10.3233/WEB-20042618:1(15-33)Online publication date: 4-Mar-2020
PostScholar: Surfacing Social Signals in Google Scholar Search
Pages 17 - 20
Abstract
PostScholar is a service that augments the results returned by Google Scholar, a search engine for academic citations. PostScholar detects the social media activity related to an article and displays that information on the search results page returned by Google Scholar. This enables Google Scholar users to interpret the social media impact of an ar-ticle, in addition to the citation impact provided by Google Scholar. The PostScholar service is implemented as a browser extension, and can be deployed across a variety of devices.
References
[1]
Bo-Christer Björk, Annikki Roos, and Mari Lauri. 2008. Global annual volume of peer reviewed scholarly articles and the share available via different Open Access options. In ELPUB2008.
[2]
Anne-Wil Harzing and Ron Van der Wal. 2007. Google Scholar: the democratization of citation analysis. Ethics in science and environmental politics 8, 1 (2007), 61-73.
Recommendations
Do altmetrics follow the crowd or does the crowd follow altmetrics?
JCDL '14: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital LibrariesChanges are occurring in scholarly communication as scientific discourse and research activities spread across various social media platforms. In this paper, we study altmetrics on the article and journal levels, investigating whether the online ...
Celebrity's self-disclosure on Twitter and parasocial relationships
This study investigated how celebrities' self-disclosure on personal social media accounts, particularly Twitter, affects fans' perceptions. An online survey was utilized among a sample of 429 celebrity followers on Twitter. Results demonstrated that ...
Seeking and sharing health information online: comparing search engines and social media
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsSearch engines and social media are two of the most com-monly used online services; in this paper, we examine how users appropriate these platforms for online health activi-ties via both large-scale log analysis and a survey of 210 people. While users ...
Comments
Information & Contributors
Information
Published In
February 2016
549 pages
ISBN:9781450339506
DOI:10.1145/2818052
- General Chairs:
- Darren Gergle,
- Meredith Ringel Morris,
- Program Chairs:
- Pernille Bjørn,
- Joseph Konstan
Copyright © 2016 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 February 2016
Check for updates
Author Tags
Qualifiers
- Abstract
Conference
CSCW '16
Sponsor:
CSCW '16: Computer Supported Cooperative Work and Social Computing
February 26 - March 2, 2016
California, San Francisco, USA
Acceptance Rates
Overall Acceptance Rate 2,235 of 8,521 submissions, 26%
Upcoming Conference
CSCW '25
- Sponsor:
- sigchi
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 158Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025
Other Metrics
Citations
Cited By
View all- Badache I(2020)2SRM: Learning social signals for predicting relevant search resultsWeb Intelligence10.3233/WEB-20042618:1(15-33)Online publication date: 4-Mar-2020
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in