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
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.
Integrity signals
Registry
Cites
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.
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
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Sentiment strength detection for the social web
Thelwall, M., Buckley, K., & Paltoglou, G. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21662 · Cited by 1,062
Social Media Analytics and Intelligence
Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010) · IEEE Intelligent Systems · doi:10.1109/mis.2010.151 · Cited by 582
Research works printed but not linked: 32
- 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
- Abrahams, 2015, An integrated text analytic framework for product defect discovery, Prod. Oper. Manage., 10.1111/poms.12303 · DOI · Paper page
- 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
- Abrahams, 2012, Vehicle defect discovery from social media, Decis. Support Syst., 54, 87, 10.1016/j.dss.2012.04.005 · DOI · Paper page
- Bellman, 1961, vol. 4
- Bose, 2008, Competitive intelligence process and tools for intelligence analysis, Ind. Manage. Data Syst., 108, 510, 10.1108/02635570810868362 · DOI · Paper page
- Cavnar, 1994, N-gram-based text categorization, Ann Arbor, MI, 48113, 161
- Chau, 2012, Business intelligence in blogs: understanding consumer interactions and communities, MIS Q., 36, 1189, 10.2307/41703504 · DOI · Paper page
- 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
- Fan, 2014, The power of social media analytics, Commun. ACM, 57, 74, 10.1145/2602574 · DOI · Paper page
- Governatori, 2011, A modeling and reasoning framework for social networks policies, Enterp. Inf. Syst., 5, 145, 10.1080/17517575.2010.513014 · DOI
- 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
- 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
- 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
- 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
- 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
- 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
- Kloptchenko, 2004, Combining data and text mining techniques for analyzing financial reports, Intell. Syst. Acc. Finance Manage., 12, 29, 10.1002/isaf.239 · DOI
- 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
- 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
- Liu, 2011, Distributed data mining for e-business, Inf. Technol. Manage., 12, 1
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zheng, 2004, Feature selection for text categorization on imbalanced data, ACM SIGKDD Explorations Newsl., 6, 80, 10.1145/1007730.1007741 · DOI · Paper page
- Zhong, 2012, Effective pattern discovery for text mining, IEEE Trans. Knowl. Data Eng., 24, 30, 10.1109/TKDE.2010.211 · DOI · Paper page
- Zhou, 2014, Social media adoption and corporate disclosure, J. Inf. Syst.
Cited by
A Big Data Analytics Method for Tourist Behaviour Analysis
Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017) · Information & Management · doi:10.1016/j.im.2016.11.011 · Cited by 393
Machine learning algorithms for social media analysis: A survey
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100395 · Cited by 276
Ledro, C., Nosella, A., & Vinelli, A. (2022) · Journal of Business & Industrial Marketing · doi:10.1108/jbim-07-2021-0332 · Cited by 216
Social media in operations and supply chain management: State-of-the-Art and research directions
Huang, S., Potter, A., & Eyers, D. (2020) · International Journal of Production Research · doi:10.1080/00207543.2019.1702228 · Cited by 60
Daly, T. M., & Ryan, J. C. (2024) · International Journal for Educational Integrity · doi:10.1007/s40979-023-00148-x · Cited by 4
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.