Automatic Detection of Irrelevant Comments in an Electronic Meeting

  • Milam Aiken School of Business Administration, University of Mississippi, University, MS 38677
  • Bart Garner School of Business Administration, University of Mississippi, University, MS 38677


Groups exchanging ideas in electronic meetings often generate irrelevant or off-topic comments that can detract from the conversation. Here, we describe a system that seeks to identify this immaterial text using previously identified keywords. Results of an experiment with the system show that group members believe meetings do have irrelevant comments that waste time, but participants often enjoy them. The system achieved an F measure of 42.3% for recall and precision, and further research is necessary to determine if this is sufficient or what can be done to improve this score.


[1] Adevaa, J., Atxaa, J., Carrillob, M., and Zengotitabengoab, E. (2014). Automatic text classification to support systematic reviews in medicine. Expert Systems with Applications, 41(4), 1498-1508.
[2] Aiken, M. (2002). Topic effects on electronic meeting comments. Academy of Information and Management Sciences, 5(1/2), 115-126.
[3] Aiken, M., Gu, L., and Wang, J. (2009). Electronic meeting topic effects. Best Practices and Conceptual Innovations in Information Resources Management: Utilizing Technologies to Enable Global Progressions. Chapter 19, Advances in Information Resources Management, Volume 20, IGI Global Books, Hershey, PA
[4] Aiken, M., Gu, L., Wang, J., and Vanjani, M. (2008). Topic influences on electronic meeting relevant comments. Issues in Information Systems, 9(2), 300-304.
[5] Aiken, M., Park, M., and Garner, B. (2012). Translation of relevant and irrelevant multilingual group support system comments. International Journal of Intercultural Information Management, 3(1), 45-58.
[6] Aiken, M., Rebman, C., and Vanjani, M. (2007). Comment generation with three electronic brainwriting techniques. Journal of Management Information and Decision Sciences, 10(1), 11-30.
[7] Aiken, M. and Waller, B. (2000). Flaming among first-time group support system users. Information and Management, 37(2), 95-100.
[8] Aiken, M., Wang, J., Gu, L., and Paolillo, J. (2011). An exploratory study of how technology supports communication in multilingual groups. International Journal of e-Collaboration, 7(1), 17-29.
[9] Alonzo, M. and Aiken, M. (2004). Flaming in electronic communication. Decision Support Systems, 36(3), 205-213.
[10] Dennis, A. and Williams, M. (2003). Electronic brainstorming. Group creativity: Innovation through collaboration, 160-178.
[11] Fitzgerald, J., Elmore, J., Koons, H., Hiebert, E., Bowen, K., Sanford-Moore, E., and Stenner, A. (2015). Important text characteristics for early-grades text complexity. Journal of Educational Psychology, 107(1), 4-29.
[12] Gu, L., Aiken, M., and Wang, J. (2007). Topic effects on process gains and losses in electronic meetings. Information Resources Management Journal, 20(4), 1-11.
[13] Hripcsak, G. and Rothschild, A. (2005). Agreement, the f-measure, and reliability in information retrieval. Journal of the American Medical Informatics Association, 12(3), 296-298.
[14] Ismailov, A., Jalil, M., Abdullah, Z., and Rahim, N. (2016). A comparative study of stemming algorithms for use with the Uzbek language. 3rd International Conference on Computer and Information Sciences (ICCOINS), 7-12.
[15] Johnson, N., Cooper, R., and Chin, C. (2008). The effect of flaming on computer-mediated negotiations. European Journal of Information Systems, 17, 417–434.
[16] Lindblom, T., Aiken, M., and Vanjani, M. (2009). Electronic facilitation of large meetings. Communications of the IIMA, 9(3), 23-38.
[17] Park, M., Aiken, M., and Ghosh, K. (2010). A study of factors affecting electronic meeting participation. International Journal of Business and Systems Research, 4(3), 264-277.
[18] Reinig, B. and Mejias, R. (2004). The effects of national culture and anonymity on flaming and criticalness in GSS-supported discussions. Small Group Research, 35(6), 698-723.
[19] Suler, J. (2004). The online disinhibition effect. Cyberpsychology & Behavior, 7(3), 321-326.
[20] Underhill, C. and Olmsted, M. (2003). An experimental comparison of computer-mediated and face-to-face focus groups. Social Science Computer Review, 21(4), 506-512.
[21] Wong, Z. and Aiken, M. (2006). The effects of time on computer-mediated communication group meetings: An exploratory study using an evaluation task. International Journal of Information Systems and Change Management, 1(2), 138-158.
[22] Xie, S., Wang, J., Amin, M., Yan, B., Bhasin, A., Yu, C., and Philip, S. (2015). A context-aware approach to detection of short irrelevant texts. In Data Science and Advanced Analytics (DSAA), 36678 2015. IEEE International Conference on (pp. 1-10). IEEE.
How to Cite
AIKEN, Milam; GARNER, Bart. Automatic Detection of Irrelevant Comments in an Electronic Meeting. INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY, [S.l.], v. 12, n. 1, p. 3123-3127, may 2017. ISSN 2278-5612. Available at: <>. Date accessed: 23 oct. 2017. doi: