The perception of useful information derived from Twitter: A survey of professionals
DOI:
https://doi.org/10.37380/jisib.v7i3.279Keywords:
Bots, Twitter, business intelligence, competitive intelligence, consumer opinion mining, sentiment analysis, social mediaAbstract
In this study we gathered data from 220 professional users of information via a
survey. Twitter is perceived as a service for useful information but not for the reason one may
expect, not because the content of the tweets give valuable information, but because of what can
be derived and extracted from the information that is being tweeted and not tweeted.
Professional users are aware that tweets are being manipulated by communication departments
so they adjust for this in their understanding of the content that is being delivered. For the
same reason “fake news” is not seen as a problem either by professionals. Twitter is seen as
valuable alongside other social media software (additional software solutions) and used directly
together with other software (integrated software solutions). As a stand-alone service it is found
to be of less value to experienced users and there are no signs that Twitter is a valuable tool for
learning.
References
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).
ACM.
Chen, H. (2010), “Business and market
intelligence 2.0, Part 2”, IEEE Intelligent
Systems, Vol. 25 No. 2, pp. 2-5. doi:
1109/MIS.2010.43.
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.
doi:10.1002/asi.23456
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.
https://doiorg.ezproxy.bib.hh.se/10.1007/s11747-014-
-3
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.
policymic.com/articles/10642/twitter-
revolution-how-the-arabspring-was-helpedby-social-media.
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
from http://cyber.law.harvard.edu/sites/
cyber.law.harvard.edu/files/Mapping_Russian
_Twitter_2012.pdf
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:
3837/tiis.2015.08.025.
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:
http://pubsonline.informs.org/doi/abs/10.1287/
isre.1120.0454
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:
1109/MIS.2010.75.
McGee, M. (2012). Twitter: 60% of users access
via mobile. Retrieved on January 22, 2013
from http://marketingland.com/twitter-60-
percentof-users-access-via-mobile-13626.
Mills, S. (2012). How Twitter is winning the 2012
US election. Retrieved on January 11, 2012
from http://www.guardian.co.uk/
commentisfree/2012/oct/16/twitter-winning-
-us-election/.
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:
London
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:
1016/j.chb.2010.04.014
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