The purpose of this study is to examine the public opinion on the Common Core State Standards (CCSS) on Twitter. Using Twitter Application Program Interface (API), we collected the tweets containing the hashtags #CommonCore and #CCSS for 12 months from 2014 to 2015. A Common Core corpus was created by compiling all the collected 660,051 tweets. The results of sentiment analysis suggest Twitter users expressed overwhelmingly negative sentiment toward the CCSS in all 50 states. Five topic clusters were detected by cluster analysis of the hashtag co-occurrence network. We also found that most of the opinion leaders were those who expressed negative sentiment toward the CCSS on Twitter. This study for the first time demonstrates how text mining techniques can be applied to education policy research, laying the foundation for real-time analytics of public opinion on education policies, thereby informing policymaking and implementation.
AbbasiA.HassanA.DharM. (2014). Benchmarking Twitter sentiment analysis tools. In CalzolariN.ChoukriK.DeclerckT.. (Eds.), Proceedings of the International Conference on Language Resources and Evaluation, Reykjavik, Iceland, 26-31 May (pp. 823-829). European Language Resources Association.
AdamicL. A.GlanceN. (2005). The political blogsphere and the 2004 U.S. election divided they blog. In AdibiJ.GrobelnikM.MladenicD.MladenicP. (Eds.), Proceedings of the Third International Workshop on Link Discovery (pp. 36-43). New York, NY: ACM. doi:10.1145/1134271.1134277
4.
AltrabshehN.CoceaM.FallahkhairS. (2015). Detecting sarcasm from students’ feedback in Twitter. In ConoleG.KlobučarT.RensingC.KonertJ.LavouéÉ. (Eds.), Design for teaching and learning in a networked world (pp. 551-555). Cham, Switzerland: Springer.
5.
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(8), 1-24.
6.
BarberáP.JostJ. T.NaglerJ.TuckerJ. A.BonneauR. (2015). Tweeting from left to right: Is online political communication more than an echo chamber?Psychological Science, 26, 1531-1542.
7.
BimberB.DavisR. (2003). Campaigning online: The Internet in U.S. elections. Oxford, UK: Oxford University Press.
8.
BodeL.HannaA.YangJ.ShahD. (2014). Candidate networks, citizen clusters, and political expression: Strategic hashtag use in the 2010 Midterms. The ANNALS of the American Academy of Political and Social Science, 659, 149-165.
9.
BonacichP. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92, 1170-1182.
10.
BorgattiS. P. (2005). Centrality and network flow. Social Networks, 27, 55-71.
11.
BorgattiS. P.EverettM. G.JohnsonJ. C. (2013). Analyzing social networks. Thousand Oaks: Sage.
12.
BorgattiS. P.HalginD. S. (2011). On network theory. Organizational Science, 22, 1168-1181.
13.
BrewerT. J.WallisM. (2015). #TFA: The intersection of social media and education reform. Critical Education, 6(14), 1-17.
14.
BrunsA.BurgessJ. E. (2011, August). The use of Twitter hashtags in the formation of ad hoc publics. Paper presented at the 6th European Consortium for Political Research General Conference, University of Iceland, Reykjavik. Retrieved from http://bit.ly/1NE6ScQ
15.
BursteinP. (2003). The impact of public opinion on public policy: A review and an agenda. Political Research Quarterly, 56, 29-40.
16.
BurtR. S. (1999). The social capital of opinion leaders. The ANNALS of the American Academy of Political and Social Science, 566, 37-54.
17.
BurtR. S. (2005). Brokerage and closure: An introduction to social capital. Oxford, UK: Oxford University Press.
18.
CarpenterJ. P.KrutkaD. G. (2014). How and why educators use Twitter: A survey of the field. Journal of Research on Technology in Education, 46, 414-434.
ChoV. (2016). Administrators’ professional learning via Twitter: The dissonance between beliefs and actions. Journal of Educational Administration, 54, 340-356.
21.
ChoV.RoJ.Littenberg-TobiasJ. (2013). What Twitter will and will not do: Theorizing about teachers’ online professional communities. Learning Landscapes, 6, 45-62
22.
ChungW.ZengD. (2015). Social-media-based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security. Journal of the Association for Information Science and Technology, 67, 1588-1606.
CoxD.McLeodS. (2014a). Social media marketing and communications strategies for school superintendents. Journal of Educational Administration, 52, 850-868.
25.
CoxD.McLeodS. (2014b). Social media strategies for school principals. NASSP Bulletin, 98, 5-25.
26.
CrooksA.CroitoruA.StefanidisA.RadzikowskiJ. (2013). #Earthquake: Twitter as a distributed sensor system. Transactions in GIS, 17(1), 124-147.
27.
DaiH.HaoJ. (2017). Mining social media data for opinion polarities about electronic cigarettes. Tobacco Control, 26, 175-180.
28.
DasS.ChenM. (2007). Yahoo! For Amazon: Sentiment extraction from small talk on the web. Journal of Management Science, 53, 1375-1388.
29.
DavisK. (2015). Teachers’ perceptions of Twitter for professional development. Disability and Rehabilitation, 37, 1551-1558.
30.
De AmorimS. G.BarthélemyJ.RibeiroC. C. (1992). Clustering and clique partitioning: Simulated annealing and tabu search approaches. Journal of Classification, 9, 17-41.
31.
DegenneA.ForseM. (1999). Introducing social networks. London, England: Sage.
32.
DemirbasM.BayirM. A.AkcoraC. G.YilmazY. S.FerhatosmanogluH. (2010, June). Crowd-sourced sensing and collaboration using Twitter. In Proceedings of 2010 IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (pp. 1-9). New York, NY: IEEE. doi:10.1109/WOWMOM.2010.5534910
ElkindP. (2016, January1). Business gets schooled: How Exxon Mobile, GE & others learned a hard lesson in the war over Common Core. Fortune. Retrieved from http://fortune.com/common-core-standards/
35.
Every Student Succeeds Act (ESSA) of 2015, Public Law No. 114-95 §§ 8526A.
36.
FikisD.WangY. (2015, November). Identifying opinion leaders and mining topics of the #EdPolicy discourse on Twitter. Paper presented at the 2015 Annual Convention of University Council for Educational Administration, San Diego, CA.
37.
FreemanL. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239.
GloverF. (1989). Tabu search—Part I. ORSA Journal on Computing, 1, 190-206.
40.
GloverF. (1990). Tabu search—Part II. ORSA Journal on Computing, 2, 4-32.
41.
GonçalvesP.AraújoM.BenevenutoF.ChaM. (2013, October7-8). Comparing and combing sentiment analysis methods. In Proceedings of the first ACM Conference on Online Social Networks, Boston, MA (pp. 27-38). New York, NY: The Association for Computing Machinery. doi:10.1145/2512938.2512951
42.
GormleyW. T.Jr. (2016). From science to policy in early childhood education. Science, 333, 978-981.
43.
GranovetterM. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360-1380.
44.
GrimmerJ.StewartB. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21, 267-297.
IyengarS.HahnK. S. (2009). Red media, blue media: Evidence of ideological selectivity in media use. Journal of Communication, 59, 19-39.
47.
JacobsenR.SnyderJ. W.SaultzA. (2014). Informing or shaping public opinion? The influence of school accountability data format on public perceptions of school quality. American Journal of Education, 121, 1-27.
48.
JahanbakhshK.MoonY. (2014). The predictive power of social media: On the predictability of U.S. Presidential Election using Twitter (arXiv: 1407.0622). Retrieved from http://arxiv.org/pdf/1407.0622v1.pdf
49.
JanssenM.WimmerM. A.DeljooA. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research. Cham, Switzerland: Springer.
50.
JuncoR.HeibergerG.LokenE. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27, 119-132.
51.
KaplanA. M.HaenleinM. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53, 59-68.
52.
KingG. (2011). Ensuring the data-rich future of the social sciences. Science, 331, 719-721.
53.
KingG. (2016). Preface: Big data is not about the data. In AlvarezR. M. (Ed.), Computational social science: Discovery and prediction (pp. 7-10). New York, NY: Cambridge University Press.
54.
KingdonJ. W. (1995). Agenda, alternatives, and public policies (2nd ed.). New York, NY: HarperCollins.
55.
KirilenkoA. P.StepchenkovaS. O. (2014). Public microblogging on climate change: One year of Twitter worldwide. Global Environmental Change, 26, 171-182.
56.
KretchmarK.SondelB.FerrareJ. J. (2018). The power of the network: Teach for America’s impact on the deregulation of teacher education. Educational Policy, 32, 423-453. doi:10.1177/0895904816637687
57.
LazerD.PentlandA.AdamicL.AralS.BarabásiA.BrewerD.. . . Van AlstyneM. (2009). Life in the network: The coming age of computational social science. Science, 323, 721-723.
LeydesdorffL. (2007). Betweenness centrality’ as an indicator of the “interdisciplinarity” of scientific journals. Journals of the American Society for Information Science and Technology, 58, 1303-1309.
60.
LinN. (2009). Social capital: A theory of social structure and action (8th ed.). New York, NY: Cambridge University Press.
61.
LopesH.PintoH. S.FranciscoA. P. (2016). Influence and sentiment homophily on Twitter social circles. In CherifiH.GoncalvesB.MenezesR.SinatraR. (Eds.), Complex Networks VII: Proceedings of the 7th Workshop on the Complex Networks CompleNet 2016 (pp. 349-361). Cham, Switzerland: Springer.
62.
McCluskeyN. (2010, February17). Behind the curtain: Assessing the case for national curriculum standards (Policy Analysis No. 661). Washington, DC: Cato Institute.
63.
McDonnellL. M. (2009). Repositioning politics in education’s circle of knowledge. Educational Researcher, 38, 417-427.
64.
McDonnellL. M. (2016). Evolving research perspectives on education politics and policy. Educational Researcher, 45, 142-148.
65.
McDonnellL. M.WeatherfordM. S. (2013). Organized interests and the Common Core. Educational Researcher, 42, 488-497.
PadgettJ. F.ArsellC. K. (1993). Robust action and the rise of the Medici, 1400-1434. American Journal of Sociology, 98, 1259-1319.
74.
PageB. I.ShapiroR. Y. (1983). Effects of public opinion on policy. American Political Science Review, 77, 175-190.
75.
PangB.LeeL. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1-135.
76.
ParkC. (2013). Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29, 1641-1648.
77.
ParkhurstJ. (2017). The politics of evidence: From evidence-based policy to the good governance of evidence. New York, NY: Routledge.
78.
PfitznerR.GarasA.SchweitzerF. (2012). Emotional divergence influences information spreading in Twitter. In Proceedings of ICWSM-12 6th International AAAI Conference on Weblogs and Social Media (pp. 543-546). Palo Alto, CA: Association for the Advancement of Artificial Intelligence Press.
79.
PolikoffM. S.HardawayT.MarshJ. A.PlankD. N. (2016). Who is opposed to Common Core and why?Educational Researcher, 45, 263-266.
80.
PorterR. E.FusarelliL. D.FusarelliB. C. (2014). Implementing the Common Core: How educators interpret curriculum reform. Educational Policy, 29, 111-139.
81.
PoschkoJ. (2011). Exploring Twitter hashtags. Retrieved from http://arxiv.org/abs/1111.6553
82.
PreethiP. G.Ajit kumarV. U. (2015). Temporal sentiment analysis and causal rules extraction from Tweets for event prediction. Procedia Computer Science, 48, 84-89.
83.
PrpićJ.TaeihaghA.MeltonJ. (2015). The fundamentals of policy crowdsourcing. Policy & Internet, 7, 340-361.
84.
RadzikowskiJ.StefanidisA.JacobsenK. H.CroitoruA.CrooksA.DelamaterP. (2016). The measles vaccination narrative in Twitter: A quantitative analysis. JMIR Public Health and Surveillance, 2(1), Article e(1). doi:10.2196/publichealth.5059
85.
RamS.ZhangW.WilliamsM.PengetnzeY. (2015). Predicting asthma-related emergency department visits using Big Data. IEEE Journal of Biomedical and Health Informatics, 19, 1216-1223.
86.
ReddickaC. G.ChatfieldbA. T.JaramilloaP. A. (2015). Public opinion on National Security Agency surveillance programs: A multi-method approach. Government Information Quarterly, 32, 129-141.
87.
RogersE. (2003). Diffusion of innovation (5th ed.). New York, NY: The Free Press.
88.
RogersR. (2015). Making public policy: The new philanthropists and American education. American Journal of Economics and Sociology, 74, 743-774.
89.
SchattschneiderE. E. (1935). Politics, pressure, and the tariff: A study of free private enterprise in pressure politics, as shown in the 1929-1930. New York, NY: Prentice Hall.
90.
SenP.DasguptaS.ChatterjeeA.SreeramP. A.MukherjeeG.MannaS. S. (2002). Small world properties of the Indian rail network. Physical Review E, 67, Article 036106. doi:10.1103/PhysRevE.67.036106
91.
ShahD. V.CappellaJ. N.NeumanW. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659, 6-13.
92.
ShavelsonR. J. (1988). Contributions of educational research to policy and practice: Constructing, challenging, changing cognition. Educational Researcher, 17(7), 4-11.
93.
SiqiZ.LinZ.JehanW.VenuV. (2011). Human as real-time sensors of social and physical events: A case study of Twitter and sports games (arXiv:1106. [cs.SI]4300). Retrieved from http://arxiv.org/abs/1106.4300
94.
SongM.MiskelC. G. (2005). Who are the influentials? A cross-state social network analysis of the reading policy domain. Educational Administration Quarterly, 41, 7-48.
StieglitzS.Dang-xuanL. (2013). Emotions and information diffusion in social media—Sentiment analysis of microblogs and sharing behavior. Journal of Management Information Systems, 29, 217-247.
97.
SupovitzJ. A.DalyA. J.Del FresnoM. (2015, February23). #CommonCore: How social media is changing the politics of education (Consortium for Policy Research in Education). Retrieved from http://repository.upenn.edu/hashtagcommoncore/1
98.
SupovitzJ. A.ReinkordtE. (2017). Keep your eye on the metaphor: The framing of the Common Core on Twitter. Education Policy Analysis Archives, 25(31), 1-29. doi:10.14507/epaa.25.2285
99.
ThelwallM.BuckleyK.PaltoglouG.CaiD.KappasA. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61, 2544-2558.
VaccariC. (2012). From echo chamber to persuasive device? Rethinking the role of the Internet in campaigns. New Media & Society, 15, 109-127.
103.
ValantJ.NewarkD. A. (2016). The politics of achievement gaps: U.S. public opinion on race-based and wealth-based differences in test scores. Educational Researcher, 45, 311-346.
WangY. (2016a). Getting personal! Twitter communication between school districts, superintendents, and the public. Journal of School Leadership, 26, 865-890.
107.
WangY. (2016b). U.S. State education agencies’ use of Twitter: Mission accomplished?SAGE Open, 6, 1-12.
108.
WangY.BowersA. J. (2016). Mapping the field of educational administration research: A journal citation network analysis. Journal of Educational Administration, 54, 242-269.
109.
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, 495-522.
110.
WassermanS.FaustK. (1994). Social network analysis: Methods and applications. New York, NY: Cambridge University Press.
111.
WattsD. J. (2013). Computational social science: Exciting progress and future directions. The Bridge: Linking Engineering and Society, 43(4), 5-11.
112.
WeilerA.GrossniklausM.SchollM. H. (2015). Run-time and task-based performance of event detection techniques for Twitter. Advanced Information Systems Engineering: Lecture Notes in Computer Science, 9097, 35-49.
113.
WhitmanW. N. (2015). Trending now: Using big data to examine public opinion of space policy. Space Policy, 32, 11-16.
114.
WidenerM. J.LiW. (2014). Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Applied Geography, 54, 189-197.
115.
WilliamsS. A.TerrasM. M.WarwickC. (2013). What do people study when they study Twitter? Classifying Twitter related academic papers. Journal of Documentation, 69, 383-410.
116.
WitherspoonC. L.StoneD. N. (2013). Analysis and sentiment detection in online reviews of tax professionals: A comparison of three software packages. Journal of Emerging Technologies in Accounting, 10, 89-115.
117.
WittemanH. O.FagerlinA.ExeN.TrottierM.Zikmund-FisherB. (2016). One-sided social media comments influenced opinions and intentions about home birth: An experimental study. Health Affairs, 35, 726-733.
118.
WohlstetterP.HoustonD. M.BuckB. (2014). Networks in New York City: Implementing the Common Core. Educational Policy, 29, 85-110.
119.
YiJ.NasukawaT.BunescuR.NiblackW. (2003). Sentiment analyzer: Extracting sentiment about a given topic using natural language processing techniques. In Proceedings of the Third IEEE International Conference on Data Mining (pp. 1-8). New York, NY: IEEE.
120.
YoungS. D.RiversC.LewisB. (2014). Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Preventive Medicine, 63, 112-115.
121.
YuenT. T.PickeringT. A. (2015, April9-12). Investigation of social networks and discussions in STEM education communities on Twitter. Paper presented at the 2015 International Conference on Learning and Teaching in Computing and Engineering, Institute of Electrical and Electronics Engineers, Taipei, China. doi:10.1109/LaTiCE.2015.20
