NDLI: TweetLDA: supervised topic classification and link prediction in Twitter

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TweetLDA: supervised topic classification and link prediction in Twitter

Content Provider	ACM Digital Library
Author	Crowcroft, Jon Quercia, Daniele Askham, Harry
Abstract	L-LDA is a new supervised topic model for assigning "topics" to a collection of documents (e.g., Twitter profiles). User studies have shown that L-LDA effectively performs a variety of tasks in Twitter that include not only assigning topics to profiles, but also re-ranking feeds, and suggesting new users to follow. Building upon these promising qualitative results, we here run an extensive quantitative evaluation of L-LDA. We test the extent to which, compared to the competitive baseline of Support Vector Machines (SVM), L-LDA is effective at two tasks: 1) assigning the correct topics to profiles; and 2) measuring the similarity of a profile pair. We find that L-LDA generally performs as well as SVM, and it clearly outperforms SVM when training data is limited, making it an ideal classification technique for infrequent topics and for (short) profiles of moderately active users. We have also built a web application that uses L-LDA to classify any given profile and graphically map predominant topics in specific geographic regions.
Starting Page	247
Ending Page	250
Page Count	4
File Format	PDF
ISBN	9781450312288
DOI	10.1145/2380718.2380750
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-22
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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