Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 234

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

A novel social media competitive analytics framework with sentiment benchmarks

He, Wu, Wu, Harris, Yan, Gongjun, Akula, Vasudeva, Shen, Jiancheng·2015·Information & Management

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.im.2015.04.006

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Research works printed but not linked: 32

  1. Abdous, 2011, Using text mining to uncover students’ technology-related problems in live video streaming, Br. J. Educ. Technol., 40, 40, 10.1111/j.1467-8535.2009.00980.x · doi:10.1111/j.1467-8535.2009.00980.x
  2. Abrahams, 2015, An integrated text analytic framework for product defect discovery, Prod. Oper. Manage., 10.1111/poms.12303 · DOI · Paper page
  3. Abrahams, 2013, What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings, Decis. Support Syst., 55, 871, 10.1016/j.dss.2012.12.023 · DOI
  4. Abrahams, 2012, Vehicle defect discovery from social media, Decis. Support Syst., 54, 87, 10.1016/j.dss.2012.04.005 · DOI · Paper page
  5. Bellman, 1961, vol. 4
  6. Bose, 2008, Competitive intelligence process and tools for intelligence analysis, Ind. Manage. Data Syst., 108, 510, 10.1108/02635570810868362 · DOI · Paper page
  7. Cavnar, 1994, N-gram-based text categorization, Ann Arbor, MI, 48113, 161
  8. Chau, 2012, Business intelligence in blogs: understanding consumer interactions and communities, MIS Q., 36, 1189, 10.2307/41703504 · DOI · Paper page
  9. Chen, 2002, CI Spider: a tool for competitive intelligence on the Web, Decis. Support Syst., 34, 1, 10.1016/S0167-9236(02)00002-7 · DOI · Paper page
  10. Fan, 2014, The power of social media analytics, Commun. ACM, 57, 74, 10.1145/2602574 · DOI · Paper page
  11. Governatori, 2011, A modeling and reasoning framework for social networks policies, Enterp. Inf. Syst., 5, 145, 10.1080/17517575.2010.513014 · DOI
  12. He, 2013, Social media competitive analysis and text mining: a case study in the pizza industry, Int. J. Inf. Manage., 33, 464, 10.1016/j.ijinfomgt.2013.01.001 · DOI · Paper page
  13. He, 2013, Examining students’ online interaction in a live video streaming environment using data mining and text mining, Comput. Hum. Behav., 29, 90, 10.1016/j.chb.2012.07.020 · DOI · Paper page
  14. He, 2013, Improving user experience with case-based reasoning systems using text mining and Web 2.0, Expert Syst. Appl., 40, 500, 10.1016/j.eswa.2012.07.070 · DOI
  15. Hung, 2012, Trends of E-Learning Research from 2000 to 2008: use of Text Mining and Bibliometrics, Br. J. Educ. Technol., 43, 5, 10.1111/j.1467-8535.2010.01144.x · DOI · Paper page
  16. Hung, 2008, Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching, MERLOT J. Online Learn. Teach., 4, 426
  17. Kim, 2007, Weighted order-dependent clustering and visualization of web navigation patterns, Decis. Support Syst., 43, 1630, 10.1016/j.dss.2006.03.007 · doi:10.1016/j.dss.2006.03.007
  18. Kloptchenko, 2004, Combining data and text mining techniques for analyzing financial reports, Intell. Syst. Acc. Finance Manage., 12, 29, 10.1002/isaf.239 · DOI
  19. Li, 2010, Using text mining and sentiment analysis for online forums hotspot detection and forecast, Decis. Support Syst., 48, 354, 10.1016/j.dss.2009.09.003 · DOI · Paper page
  20. Lin, 2014, Determinants of users’ continuance of social networking sites: a self-regulation perspective, Inf. Manage., 51, 595, 10.1016/j.im.2014.03.010 · DOI · Paper page
  21. Liu, 2011, Distributed data mining for e-business, Inf. Technol. Manage., 12, 1
  22. Ludwig, 2013, More than words: the influence of affective content and linguistic style matches in online reviews on conversion rates, J. Marketing, 77, 87, 10.1509/jm.11.0560 · DOI · Paper page
  23. Romero, 2008, Data mining in course management systems: moodle case study and tutorial, Comput. Educ., 51, 368, 10.1016/j.compedu.2007.05.016 · DOI · Paper page
  24. T.O. Sprenger, I.M. Welpe, Tweets and trades: The information content of stock microblogs, November 1, 2010, (available at SSRN: http://ssrn.com/abstract=1702854) or http://dx.doi.org/10.2139/ssrn.1702854. · DOI
  25. Stieglitz, 2013, Social media and political communication: a social media analytics framework, Soc. Netw. Anal. Min., 3, 1277, 10.1007/s13278-012-0079-3 · DOI · Paper page
  26. Teo, 2001, Assessing the impact of using the Internet for competitive intelligence, Inf. Manage., 39, 67, 10.1016/S0378-7206(01)00080-5 · DOI · Paper page
  27. Trainor, 2013, Effects of relational proclivity and marketing intelligence on new product development, Marketing Intell. Plann., 31, 788, 10.1108/MIP-02-2013-0028 · DOI
  28. Wang, 2013, ExpertRank: a topic-aware expert finding algorithm for online knowledge communities, Decis. Support Syst., 54, 1442, 10.1016/j.dss.2012.12.020 · DOI · Paper page
  29. Xu, 2011, Mining comparative opinions from customer reviews for competitive intelligence, Decis. Support Syst., 50, 743, 10.1016/j.dss.2010.08.021 · DOI · Paper page
  30. Zheng, 2004, Feature selection for text categorization on imbalanced data, ACM SIGKDD Explorations Newsl., 6, 80, 10.1145/1007730.1007741 · DOI · Paper page
  31. Zhong, 2012, Effective pattern discovery for text mining, IEEE Trans. Knowl. Data Eng., 24, 30, 10.1109/TKDE.2010.211 · DOI · Paper page
  32. Zhou, 2014, Social media adoption and corporate disclosure, J. Inf. Syst.

Cited by

See all 234

Metadata

Authors
He, W., Wu, H., Yan, G., Akula, V., & Shen, J.
Venue
Information & Management
Publisher
Elsevier BV
Published
2015-11
Volume
52
Issue
7
Pages
801-812
ISSN
0378-7206 (ISSN-L)
OpenAlex
W264248106
Cited by
234
References
4
FWCI
21.27
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A novel social media competitive analytics framework with sentiment benchmarks. https://app.cochat.ai/papers/10.1016/j.im.2015.04.006
The paper
He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015). A novel social media competitive analytics framework with sentiment benchmarks. Information & Management. https://doi.org/10.1016/j.im.2015.04.006

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.