Grounded in the sense-making literature, this study explores the discourse around and sentiment toward the newly passed Every Student Succeeds Act (ESSA) as discussed on the social media platform Twitter. The study explores the characteristics of and connections between users, the content of posts, and the sentiment toward ESSA. Data consisted of 12,544 tweets posted by 8,063 users analyzed through discourse analysis and social network analysis. Findings suggest that the discourse appears generally nonnegative with a clear pattern of clustering around sentiment. This suggests that users are in discourse with other users who share their view of the law. Discussion of policy issues such as local implementation and equity were common as were discussions of teacher preparation and comparisons to NCLB. Implications for the ongoing implementation of ESSA are discussed.
CarpenterJ. P., & KrutkaD. G. (2014). How and why educators use Twitter: A survey of the Held. Journal of research on technology in education, 46(4), 414–434.
CoburnC. E. (2001). Collective sensemaking about reading: How teachers mediate reading policy in then professional communities. Educational evaluation and policy analysis, 23(2), 145–170.
11.
CoburnC. E. (2005). Shaping teacher sensemaking: School leaders and the enactment of reading policy. Educational policy, 19(3), 476–509.
12.
CohenM. D., MarchJ. G., & OlsenJ. P. (1972). A garbage can model of organizational choice. Administrative science quarterly, 1–25.
13.
DalyA. J., LiouY. H., TranN. A., CornelissenF., & ParkV. (2014). The rise of neurotics social networks, leadership, and efficacy in district reform. Educational Administration Quarterly, 50(2), 233–278.
14.
DatnowA., & ParkV. (2009). Conceptualizing policy implementation: Large-scale reform in an era of complexity. Handbook of education policy research, 348–361.
15.
DenistonR. D., & GerrityK. W. (2011). Elementary school teachers’ perceptions of No Child Left Behind and its effect on morale. Scholarly Partnerships, 5(2), 4.
ElderJ., MinerG., NisbettB., DelenD., FastA., & HillT. (2012). Practical text mining and statistical analysis of non-structured text data applications.Academic Press, Elsevier.Waltham, MA.
19.
EvelandW. P., & HivelyM. H. (2009). Political discussion frequency, network size, and “heterogeneity” of discussion as predictors of political knowledge and participation. Journal of Communication, 59(2), 205–224.
20.
Every Student Succeeds Act of 2015, U.S.C. § 1177 (2015).
21.
GersonJ. (2007). NCLB: Bad for teachers, bad for kids. United Teacher, 37(2), 14.
22.
HuM., & LiuB. (2004). Mining and summarizing customer reviews. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), Aug 22–25, 2004, Seattle, Washington, USA.
KolibaC., MeekJ.W., & ZiaA. (2011). Governance networks in public administration and public policy.Boca Raton, FL: CRC Press.
27.
LeeJ., & MyersT. (2016). Can social media change your mind? SNS use, crosscutting exposure and discussion, and political view change. Social Media Studies, 2(2), 87–97.
28.
LiuB., HuM., & ChengJ. (2005). Opinion observer: Analyzing and comparing opinions on the web. Proceedings of the 14th International World Wide Web conference (www-2005), May 10–14, 2005, Chiba, Japan.
29.
MarwickA. E. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114–133.
30.
MazurE., & RichardsL. (2011). Adolescents’ and emerging adults’ social net. working online: Homophily or diversity?. Journal of Applied Developmental Psychology, 32(4), 180–188.
31.
McLendonM., & Cohen-VogelL. (2008). Understanding educational policy-making in the American states: Lessons from political science. In CooperB. S., FusarelliL., and CibulkaJ. (Eds.), Handbook of Educational Politics and Policy (pp. 30–51). Oxford, UK: Routledge, Taylor & Francis.
32.
McLeodJ. M., ScheufeleD. A., & MoyP. (1999). Community, communication, and participation: The role of mass media and interpersonal discussion in local political participation. Political communication, 16(3), 315–336.
33.
McPhersonM., Smith-LovinL., & CookJ. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 415–444.
34.
MinerG., ElderJ., HillT., NisbetR., DelenD., & FastA. (2012). Practical text mining and statistical analysis for non-structured text data applications.Academic Press: Waltham, MA.
35.
NicholsS. L., & BerlinerD. C. (2008). Why has high-stakes testing so easily slipped into contemporary American life?. The Phi Delta Kappan, 89(9), 672–676.
36.
NigamK., McCallumA., ThrunS., & MitchellT. (1998). Learning to classify text from labeled and unlabeled documents. AAAI/IAAI, 792.
SandfortJ., & MoultonS. (2015). Effective implementation in practice: Integrating public policy and management.San Francisco, CA: Jossey-Bass.
41.
SchauH. J., & GillyM. C. (2003). We are what we post? Self-presentation in personal web space. Journal of Consumer Research, 30(3), 385–404.
42.
ScheufeleD. A., NisbetM. C., BrossardD., & NisbetE. C. (2004). Social structure and citizenship: Examining the impacts of social setting, network heterogeneity, and informational variables on political participation. Political Communication, 21(3), 315–338.
43.
SiskinL. S. (1994). Realms of knowledge: Academic departments in secondary schools.Routledge.
SpillaneJ. P. (2004). Standards deviation: How schools misunderstand education policy.Harvard University Press.
46.
SpillaneJ.P., ReiserB.J., & ReimerT. (2002). Policy implementation and cognition: Reframing and refocusing implementation research. Review of Educational Research, 72(3): 387–431.
47.
StrapparavaC., & ValituttiA. (2004). WordNet-Affect: An affective extension of WordNet. In Proc. Of 4th International Conference on Language Resources and Evaluation (LREC 2004), p. 1083–1086, Lisbon.
48.
SundermanG. L., TraceyC. A., KimJ., & OrfieldG. (2004). Listening to teachers: Classroom realities and No Child Left Behind.Cambridge, MA: The Civil Rights Project at Harvard University.
SupovitzJ., DalyA., del FresnoM. (2015). #CommonCore: How social media is changing the politics of education.University of Pennsylvania Scholarly Commons. Retrieved from; http://www.hashtagcommoncore.com.
WaltherJ. B. (2007). Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition. Computers in Human Behavior, 23(5), 2538–2557.
53.
WangY., SauersN., & RichardsonJ. (2016). A social network approach to examine K-12 educational leaders’ influence on information diffusion on Twitter. Journal of School Leadership, 26(4), 495–522.
54.
WattsD. J., & DoddsP. S. (2007). Influentials, networks, and public opinion formation. Journal of consumer research, 34(4), 441–458.
55.
WeareC., MussoJ., & JunK. N. (2009). Cross-talk: The role of homophily and elite bias in civic associations. Social Forces, 88(1), 147–173.
56.
WeickK. E. (1995). Sensemaking in organizations (Vol. 3). Sage.
57.
WilsonT., WiebeJ., & HoffmannP. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. Proc. of HLT-EMNLP-2005.
58.
WongK. K. (2008). Federalism, equity, and accountability in education. Handbook of education politics and policy, 19–29.
59.
YardiS., & BoydD. (2010). Dynamic debates: An analysis of group polarization over time on twitter. Bulletin of Science, Technology & Society, 30(5), 316–327.
60.
ZellmerM. B., FrontierA., & PheiferD. (2006). What are NCLB's instructional costs?. Educational Leadership, 64(3), 43.
