This study investigated the relationship between the leader's gender, leadership position. Twitter use, and influence on information diffusion in the communication network on Twitter. We collected the 30,200 latest tweets of 151 active Twitter users who held educational leadership positions. Results of social network analysis and multiple regression analyses suggest a gender inequality in the leader's influence on information diffusion in the network. Findings also indicate no significant relationship between leadership position (district vs. building) and a leader's influence in the network. Moreover, Twitter following was positively associated with the leader's influence in the network, whereas the number of followers, weekly tweets, and the time of Twitter account creation did not predict the leader's influence. Practical implications on how leaders use Twitter to disseminate information are discussed.
AdiA., EricksonK., & LillekerD. G. (2014). Elite tweets: Analyzing the Twitter communication patterns of Labour Party peers in the House of Lords.Policy Internet6(1), 1–27.
2.
AuW., & FerrareJ. J. (2014). Sponsors of policy: A network analysis of wealthy elites, their affiliated philanthropies, and charter school reform in Washington State.Teachers College Record,116, 080306.
3.
BaeY., & LeeH. (2012). Sentiment analysis of Twitter audiences: Measuring the positive or negative influence of popular Twitterers.Journal of the American Society for Information Science and Technology63(12), 2521–2535.
4.
BakshyE., HolmanJ. M., MasonW. A., & WattsD. J. (2011). Everyone's an influencer: Quantifying influence on twitter. Proceedings from 2011 the fourth ACM In ternational Conference on Web Search and Data Mining.New York, NY: ACM.
BorgattiS. P. (2005). Centrality and network flow.Social Networks,27, 55–71.
7.
BorgattiS. P. (2006). Identifying sets of key players in a network.Computational, Mathematical’ and Organizational Theory,12(1), 21–34.
8.
BorgattiS. P., & CrossR. (2003). A relational view of information seeking and learning in social networks.Management Science,49(4), 432–445.
9.
BorgattiS. P., & EverettM. G. (1992). Notions of position in social network analysis.Sociological, Methodology,22, 1–35.
10.
BurtR. S. (1976). Positions in networks.Social, Forces, 55(1), 93–122.
11.
BurtR. S. (1999). The social capital of opinion leaders.The Annals of the American Academy of Political and Social, Science, 566(1), 37–54.
12.
BurtR. S. (2005). Brokerage and closure: an introduction to social, capital.Oxford, UK: Oxford University Press.
13.
CarpenterJ. P., & KrutkaD. G. (2014). How and why educators use Twitter: A survey of the field.Journal of Research on Technology in Education,46(4) 414–434.
14.
ChaM., HaddadiH., BenevenutoF., & GummadiK. P. (2010). Measuring user influence in twitter: The million follower fallacy. Proceedings from 2010 International, AAAI Conference on Weblogs and Social, Media. Retrieved from http://snap.stanford.edu/class/cs224w-readings/cha10influence.pdf.
15.
ChoV., RoJ., & Littenberg-TobiasJ. (2013). What Twitter will and will not do: Theorizing about teachers’ online professional communities.Learning Landscapes6(2), 45–62.
16.
CialdiniR. B., & GoldsteinN. J. (2004). Social influence: Compliance and conformity.Annual, Review of Psychology (1), 591–621.
17.
CoburnC., & RussellJ. (2008). Getting the most out of professional learning communities and coaching: Promoting interactions that support instructional improvement.Learning Policy Brief1(3), 1–5.
18.
ColeR. P., & WeinbaumE. H. (2010). Changes in attitude: Peer influence in high school reform. In DalyA. J. (Ed.), Social, network theory and educational, change (pp. 77–96). Cambridge, MA: Harvard University Press.
19.
CoxD., & McLeodS. (2014). Social media marketing and communications strategies for school superintendents.Journal of Educational Administration,52(6), 850–868.
20.
CresciS., PetrocchiM., SpognardiA., TesconiM., & PietroR. D. (2014). A criticism to society (as seen by Twitter analytics). Proceedings from IEEE 34 International Conference on Distributed Computing Systems Workshops, Madrid, Spain, 30 June-3 July (pp. 194–200). IEEE Computer Society. Doi: 10.1109/ICDCSW.2014.31
21.
DalyA. J. (2012). Data, dyads, and dynamics: Exploring data use and social networks in educational improvement.Teachers College Record,114(11), EJ1001994.
22.
DalyA. J., & FinniganK. (2012). Exploring the space between: Social networks, trust, and urban school district leaders.Journal of School Leadership,22(3), 493–530.
23.
DalyA. J., LiouY., IranN. A., CornelissenF., & ParkV. (2014). The rise of neurotics: Social networks, leadership, and efficacy in district reform.Educational, Administration Quarterly,50(2), 233–278.
24.
DegenneA., & ForseM. (1999). Introducing social networks.London, UK: Sage.
FinniganK., & DalyA. J. (2012). Mind the gap: Learning, trust, and relationships in an underperforming urban district.American Journal of Education, 119(1), 41–71.
27.
FreemanL. C. (1977). A set of measures of centrality based upon betweenness.Sociometry,40, 35–41.
28.
FreemanL. C. (1979). Centrality in social networks. Conceptual clarification.Social Networks1(3), 223–258.
29.
GruzdA., & WellmanB. (2014). Networked influence in social media: Introduction to the special issue.American Behavioral. Scientists,58(10), 1251–1259.
30.
HaslamS. A., ReicherS. D., & PlatowM. J. (2011). The new psychology of leadership: Identity, influence, and power.NY, NY: Psychology Press.
31.
HerringS. C., KouperI., ScheidtL. A., & WrightE. L. (2004). Women and children last: The discursive construction of weblogs. In GurakL., AntonijevicS., JohnsonL., RatliffC., & ReymanJ. (Eds.), Into the Blogosphere: Rhetoric., community, and culture of weblogs.Minneapolis: University of Minnesota. Retrieved from http://blog.lib.umn.edu/blogosphere/women_and_children.html
32.
HubermanA. B., & AdamicL. A. (2004). Information dynamics in the networked world.Lecture notes in Physics,650, 371–398.
33.
JavaA., FininT., SongX., & TsengB. (2007). Why we Twitter: Understanding microblogging usage, and communities. Proceedings of the 9th WebKDD and 1st SNA-KDD workshop on Web mining, San Jose, CA. New York, NY: Association for Computing Machinery.
34.
JuncoR., HeibergerG., & LokenE. (2011). The effect of Twitter on college student engagement and grades.Journal of Computer Assisted Learning,27(2), 119–132.
35.
KilduffM., & KrackhardtD. (2008). Interpersonal, networks in organizations: Cognition, personality, dynamics, and culture: Structural analysis in the social sciences.New York, NY: Cambridge University Press.
36.
LinN. (1976). Foundations of social, research.New York, NY: McGraw-Hill.
37.
LinN. (2009). Social capital: A theory of social structure and action (8th ed.).New York, NY: Cambridge University Press.
MoolenaarN., DalyA. J., & SleegersP. (2011). Ties with potential: Social network structure and innovation in Dutch schools.Teachers College Record,113(9) 1983–2017.
40.
OtterbachersJ., ShapiroM.A., & HemphillL. (2013). Interacting or just acting? A case study of European, Korean, and American politicians? Interactions with the public on Twitter.Journal of Contemporary Eastern Asia,12(1), 5–20.
41.
PedersenS., & MacafeeC. (2007). Gender differences in British blogging.Journal of Computer-Mediated Communication,12(4), 1472–1492.
42.
RatliffC. (2006). “Where are the women?” Rhetoric and gender in weblog discourse (Unpublished doctoral dissertation). University of Minnesota, Minnesota.
43.
RavenB. H., SchwarzwaldJ., & KoslowskyM. (1998). Conceptualizing and measuring a power/interaction model of interpersonal influence.Journal of Applied Social Psychology,28(4), 307–322.
44.
RogersE. (2003). Diffusion of innovation (5th ed.). New York, NY: Free Press.
45.
SabidussiG. (1966). The centrality index of a graph.Psychometrika,31, 581–603.
46.
SauersN. J., & RichardsonJ. (2015). Learning by following: An analysis of how K-12 school leaders use Twitter.NASSP Bulletin, doi: 10.1177/0192636515583869.
47.
SlaugherA. J., YuJ., & KoehlyL. M. (2009). Social network analysis: Understanding the role of context in small groups and organizations. In SalasE., GoodwinG. F., & BurkeC. S. (Eds.), Team effectiveness in complex organizations (pp. 433–459). New York, NY: Taylor & Francis Group.
48.
StieglitzS., & Dang-XuanL. (2013). Emotions and information diffusion in social media-sentiment of microblogs and sharing behavior.Journal of Management Information Systems,29(4), 217–247.
49.
ThackerayR., NeigerB. L., SmithA. K., & WagenenS. B. V. (2012). Adoption and use of social media among public health departments.BMC Public Health,12(1), 242. Retrieved from http://www.biomedcentral.com/1471-2458/12/242.
50.
TorrenteE., MartiT., & EscarrabillJ. (2012). A breath of Twitter.Revista Portuguesa de Pneumonologia,18(3), 137–141. doi: 10.1016/j.rppneu.2012.02.007.
VeletsianosG. (2011). Higher education scholars’ participation and practices on Twitter.Journal of Computer Assisted Learning,28(4), 336–349.
56.
WangY. (2013). Social media in schools: A treasure trove or hot potato?Journal of Cases in Educational Leadership,16(1), 83–91.
57.
WassermanS., & FaustK. (1994). Social, network analysis: Methods and. applications.New York, NY: Cambridge University Press.
58.
WengJ., LimE., JiangJ., & HeQ. (2010). Twitterrank: Finding topic-sensitive influential twitterers. Proceedings of 2010 the third. ACM International Conference on Web Search, and Data Mining.New York, NY: ACM.
59.
XuW. W., SangY., BlasiolaS., & ParkH. W. (2014). Predicting opinion leaders in Twitter activism networks: The case of the Wisconsin recall election.American Behavioral Scientists,58(10), 1278–1293.
60.
ZhangY., WuJ., & MaL. (2014). A multi-agent school simulation based hierarchical social networks.Intelligent Information Management,6, 196–210.
