The perception of useful information derived from Twitter: A survey of professionals


  • Klaus Solberg Søilen
  • Gerson Tontini
  • Ulf Aagerup



Bots, Twitter, business intelligence, competitive intelligence, consumer opinion mining, sentiment analysis, social media


In this study we gathered data from 220 professional users of information via asurvey. Twitter is perceived as a service for useful information but not for the reason one mayexpect, not because the content of the tweets give valuable information, but because of what canbe derived and extracted from the information that is being tweeted and not tweeted.Professional users are aware that tweets are being manipulated by communication departmentsso they adjust for this in their understanding of the content that is being delivered. For thesame reason “fake news” is not seen as a problem either by professionals. Twitter is seen asvaluable alongside other social media software (additional software solutions) and used directlytogether with other software (integrated software solutions). As a stand-alone service it is foundto be of less value to experienced users and there are no signs that Twitter is a valuable tool forlearning.


Alsaedi, N., Burnap, P., & Rana, O. (2017). Can

we predict a riot? disruptive event detection

using twitter. ACM Transactions on Internet

Technology (TOIT), 17(2), 18.

Amara, Y., Søilen, K. S., & Vriens, D. (2012d).

Using the SSAV model to evaluate Business

Intelligence Software. Journal of Intelligence

Studies in Business, 2(3).

Asai, S. (2009). Sales patterns of hit music in

Japan. Journal of Media Economics, 22, 81–

Castillo, C., Mendoza, M., & Poblete, B. (2011,

March). Information credibility on twitter.

In Proceedings of the 20th international

conference on World wide web (pp. 675-684).


Chen, H. (2010), “Business and market

intelligence 2.0, Part 2”, IEEE Intelligent

Systems, Vol. 25 No. 2, pp. 2-5. doi:


Chen, H. and Zimbra, D. (2010), “AI and opinion

mining”, IEEE Intelligent Systems, Vol. 25

No. 3, pp. 74-76. doi: 10.1109/MIS.2010.75.

Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S.

(2012). Detecting automation of Twitter

accounts: Are you a human, bot, or cyborg?

IEEE Transactions on Dependable and Secure

Computing, 9(6), 811–824

Fougatsaro, V. (2009). A study of open source

ERP systems. Master Thesis. Blekinge

Institute of Technology, Sweden.

Haustein, S., Bowman, T. D., Holmberg, K., Tsou,

A., Sugimoto, C. R. and Larivière, V. (2016),

Tweets as impact indicators: Examining the

implications of automated “bot” accounts on

Twitter. J Assn Inf Sci Tec, 67: 232–238.


Hayes, D. (2002). Tentpoles fade fast after

opening ad blast. Variety Magazine, 4, 2002.

Hennig-Thurau, T., Wiertz, C. & Feldhaus, F. J.

of the Acad. Mark. Sci. (2015) 43: 375.


Hong, L., Dan, O., & Davison, B. D. (2011,

March). Predicting popular messages in

twitter. In Proceedings of the 20th

international conference companion on World

wide web (pp. 57-58). ACM.

Inkpen, D., Liu, J., Farzindar, A., Kazemi, F., &

Ghazi, D. (2017). Location detection and

disambiguation from Twitter

messages. Journal of Intelligent Information

Systems, 49(2), 237-253.

Java, A., Song, X., Finin, T., & Tseng, B. (2007,

August). Why we twitter: understanding

microblogging usage and communities.

In Proceedings of the 9th WebKDD and 1st

SNA-KDD 2007 workshop on Web mining and

social network analysis (pp. 56-65). ACM.

Jenster, P., & Søilen, K. S. (2013). The

Relationship between Strategic Planning and

Company Performance–A Chinese

perspective. Journal of Intelligence Studies in

Business, 3(1).

Kassim, S. (2012). Twitter revolution: how the

Arab Spring was helped by social media.

Retrieved January 11, 2012 from http://www.


Kelly, J., Barash, V., Alexanyan, K., Etling, B.,

Faris, R., Gasser, U., & Palfrey, J. (2012).

Mapping Russian Twitter. Berkman Center

Research Publication, (2012–3). Retrieved



Kim, Y., Dwivedi, R., Zhang, J., & Jeong, S. R.

(2016). Competitive intelligence in social

media Twitter: iPhone 6 vs. Galaxy S5. Online

Information Review, 40(1), 42-61.

Kim, Y., Kwon, D.Y. and Jeong, S.R. (2015),

“Comparing machine learning classifiers for

movie WOM opinion mining”, KSII

Transactions on Internet and Information

Systems, Vol. 9 No. 8, pp. 3178-3190. doi:


Kim, Y. and Jeong, S.R. (2015), “Opinion-mining

methodology for social media analytics”, KSII

Transactions on Internet and Information

Systems, Vol. 9 No. 1, pp. 391-406.

Li, Z. and Li, C. (2014), “Twitter as a social actor:

how consumers evaluate brands differently on

Twitter based on relationship norms”,

Computers in Human Behavior, Vol. 39, pp.

-196. doi: 10.1016/j.chb.2014.07.016.

Liu, Y., Chen, Y., Lusch, R.F., Chen, H., Zimbra,

D. and Zeng, S. (2010), “User-generated

content on social media: predicting market

success with online word-on-mouth”, IEEE

Intelligent Systems, Vol. 25 No. 1, pp. 8-12.

doi: 10.1109/MIS.2010.75.

Lu, X., Ba, S., Huang, L. and Feng, Y. (2013),

“Promotional marketing or word-of-mouth?

Evidence from online restaurant reviews”,

Information Systems Research, Vol. 24 No. 3,

pp. 596-612, available at:


Lusch, R.F., Liu, Y. and Chen, Y. (2010), “The

phase transition of markets and

organizations: the new intelligence and

entrenreneurial frontier”, IEEE Intelligent

Systems, Vol. 25 No. 1, pp. 5-8. doi:


McGee, M. (2012). Twitter: 60% of users access

via mobile. Retrieved on January 22, 2013



Mills, S. (2012). How Twitter is winning the 2012

US election. Retrieved on January 11, 2012




Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A.

C. (2011, June). Bad news travel fast: A

content-based analysis of interestingness on

twitter. In Proceedings of the 3rd International

Web Science Conference (p. 8). ACM.

Porter, M. E., & Millar, V. E. (1985). How

information gives you competitive advantage.

Sabanovic, A., & Søilen, K. S. (2012). Customers’

Expectations and Needs in the Business

Intelligence Software Market. Journal of

Intelligence Studies in Business, 2(1).

Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010,

August). Want to be retweeted? large scale

analytics on factors impacting retweet in

twitter network. In Social computing

(socialcom), 2010 ieee second international

conference on (pp. 177-184). IEEE.

Søilen, K. S. (2015). A place for intelligence

studies as a scientific discipline. Journal of

Intelligence Studies in Business, 5(3), 35-46.

Søilen, K. S. (2012b). An evaluation of Business

Intelligence Software systems in SMEs–a case

study. Journal of Intelligence Studies in

Business, 2(2).

Søilen, K. S., Kovacevic, M. A., & Jallouli, R.

(2012). Key success factors for Ericsson mobile

platforms using the value grid model. Journal

of Business Research, 65(9), 1335-1345.

Søilen, K. S. (2012a). The fallacy of the service

economy: a materialist perspective. European

Business Review, 24(4), 308-319.

Søilen, K. S. (2012c). Geoeconomics. Bookboon:


Ye, Q., Law, R., Gu, B. and Chen, W. (2011), “The

influence of user-generated content on

traveler behavior: an empirical investigation

on the effects of e-word-of-mouth to hotel

online bookings”, Computers in Human

Behavior, Vol. 27 No. 2, pp. 634-639. doi: