Since its inception, computational social science (CSS) has been characterized as an interdisciplinary field. Nevertheless, unlike a mature interdisciplinary field that duly integrates knowledge beyond disciplinary boundaries, CSS has arguably been fragmented into multiple lines of inquiry. Although such fragmentation has been a constant concern, limited empirical evidence exists to substantiate any degree of integration in research on CSS. To systematically map the landscape of research on CSS, we examined the dynamic topology of the bibliometric network of CSS during the past 20 years. By comparing the structure of the coupling network and the co-subject network with three prototypical network models that we simulated—the centralized model, the fragmented model, and the cohesive model—our analysis revealed that the citation networks of research on CSS are highly centralized but less fragmented than often assumed. Beyond that, a driving factor shaping the coupling network’s cohesive structure is the citation to high-impact articles. Those and other findings contribute to current understandings of the process of integration in the evolution of disciplines.
AboelelaS. W.LarsonE.BakkenS.CarrasquilloO.FormicolaA.GliedS. A.GebbieK. M. (2007). Defining interdisciplinary research: Conclusions from a critical review of the literature. Health Services Research, 42(1), 329–346. https://doi.org/10.1111/j.1475-6773.2006.00621.x
2.
ApostelL.BergerG.BriggsA.MichaudG. (1972). Interdisciplinarity problems of teaching and research in universities. Organization for Economic Cooperation and Development.
3.
BakshyE.MessingS.AdamicL. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160
BarabâsiA. L.JeongH.NédaZ.RavaszE.SchubertA.VicsekT. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and Its Applications, 311(3–4), 590–614. https://doi.org/10.1016/S0378-4371(02)00736-7
6.
BettencourtL. M.KaiserD. I.KaurJ. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics, 3(3), 210–221. https://doi.org/10.1016/j.joi.2009.03.001
7.
BirnbaumP. H. (1982). The organization and management of interdisciplinary research, a progress report. Journal of the Society of Research Administrators, 13(4), 11–24.
BorgattiS. P.MehraA.BrassD. J.LabiancaG. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895. https://doi.org/10.1126/science.1165821
10.
BoydD.CrawfordK. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
11.
CarolanB. V. (2008). The structure of educational research: The role of multivocality in promoting cohesion in an article interlock network. Social Networks, 30(1), 69–82. https://doi.org/10.1016/j.socnet.2007.08.001
12.
ChenH.ChiangR. H.StoreyV. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
ChinW. W.MarcolinB. L.NewstedP. R. (2003). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018.
15.
ChoiB.PakA. (2006). Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. Clinical and Investigative Medicine, 29(6), 351–364.
16.
ColleoniE.RozzaA.ArvidssonA. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317–332. https://doi.org/10.1111/jcom.12084
17.
CountsS.De ChoudhuryM.DiesnerJ.GilbertE.GonzalezM.KeeganB. (2014). Computational social science: CSCW in the social media era. Proceedings of the Companion Publication of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW Companion (pp. 105–108). Association for Computing Machinery. https://doi/10.1145/2556420.2556849
18.
DandekarP.GoelA.LeeD. T. (2013). Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences, 110(15), 5791–5796. https://doi.org/10.1073/pnas.1217220110
19.
DavidssonP.HonigB. (2003). The role of social and human capital among nascent entrepreneurs. Journal of Business Venturing, 18(3), 301–331. https://doi.org/10.1016/S0883-9026(02)00097-6
FolkeC. (2006). Resilience: The emergence of a perspective for social–ecological systems analyses. Global Environmental Change, 16(3), 253–267. https://doi.org/10.1016/j.gloenvcha.2006.04.002.
23.
FortunatoS.BergstromC. T.BörnerK.EvansJ. A.HelbingD.MilojevićS.PetersenA. M.RadicchiF.SinataraR.UzziB.VespignaniA.WaltmanL.WangD.BarabásiA. L. (2018). Science of science. Science, 359(6379), eaao0185. https://doi.org/10.1126/science.aao0185
24.
FosterJ. G.RzhetskyA.EvansJ. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review, 80(5), 875–908. https://doi.org/10.1177/0003122415601618
25.
FuchsmanK. (2009). Rethinking integration in interdisciplinary studies. Issues in Interdisciplinary Studies, 27(2009), 70–85.
26.
FullerS. (2010). Deviant interdisciplinarity. In FrodemanR. (Ed.), The Oxford handbook of interdisciplinarity (pp. 50–64). Oxford University Press.
27.
GarfieldE.MalinM.SmallH. (1983). Citation data as science indicators. Information Scientist, 6, 580–608. (Reprinted from “Toward a metric of science: The advent of science indicators,” 1978. New York: John Wiley & Sons).
28.
GolderS. A.MacyM. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129–152. https://doi.org/10.1146/annurev-soc-071913-043145
29.
HaneefF.AbbasiR. A.SindhuM. A.KhattakA. S.NoorM. N.AljohaniN. R.ArafatS. (2020). Using network science to understand the link between subjects and professions. Computers in Human Behavior, 106(3), 106228. https://doi.org/10.1016/j.chb.2019.106228
30.
HilbertM.BarnettG.BlumenstockJ.ContractorN.DiesnerJ.FreyS.González-BailónS.LambersonP.PanJ.PengT.ShenC.SmaldinoP.van AtteveldtW.WaldherrA.ZhangJ.ZhuJ. (2019). Computational communication science: A methodological catalyzer for a maturing discipline. International Journal of Communication, 13(2019), 3912–3934. https://ijoc.org/index.php/ijoc/article/view/10675/2764.
31.
HofmanJ. M.WattsD. J.AtheyS.GaripF.GriffithsT. L.KleinbergJ.MargettsH.MullainathanS.SalganikM. J.VazireS.VespignaniA.YarkoniT. (2021). Integrating explanation and prediction in computational social science. Nature, 595(7866), 181–188. https://doi.org/10.1038/s41586-021-03659-0.
32.
HuJ.ZhangY. (2017). Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization. Scientometrics, 112(1), 91–109. https://doi.org/10.1007/s11192-017-2383-1
33.
HuangY.SchuehleJ.PorterA. L.YoutieJ. (2015). A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data. Scientometrics, 105(3), 2005–2022. https://doi.org/10.1007/s11192-015-1638-y
34.
HuutoniemiK.KleinJ. T.BruunH.HukkinenJ. (2010). Analyzing interdisciplinarity: Typology and indicators. Research Policy, 39(1), 79–88. https://doi.org/10.1016/j.respol.2009.09.011
JantschE. (1970). Inter- and transdisciplinary university: A systems approach to education and innovation. Policy Sciences, 1(4), 403-428. https://doi.org/10.1007/BF00145222
37.
KarlovčecM.MladenićD. (2015). Interdisciplinarity of scientific fields and its evolution based on graph of project collaboration and co-authoring. Scientometrics, 102(1), 433–454. https://doi.org/10.1007/s11192-014-1355-y
KleinJ. T. (1996). Crossing boundaries: Knowledge, Disciplinarities, and Interdisciplinarities. University Press.
40.
KleinJ. T. (2010). A taxonomy of interdisciplinarity. In FrodemanR. (Ed.), The Oxford handbook of interdisciplinarity (pp. 16–30). Oxford University Press.
41.
KleinJ. T.NewellW. H. (1997). Advancing interdisciplinary studies. In GaffJ.RatcliffeJ. (Eds.), Handbook of the undergraduate curriculum: A comprehensive guide to purposes, structures, practices, and change. Jossey-Bass.
42.
KrohnW. (2010). Interdisciplinary case and disciplinary knowledge. In FrodemanR. (Ed.), The Oxford handbook of interdisciplinarity (pp. 31–49). Oxford University Press.
43.
KuhnT. S. (2012). The structure of scientific revolutions. University of Chicago press.
LattucaL. R. (2001). Creating interdisciplinarity: Interdisciplinary research and teaching among college and university faculty. Vanderbilt University Press.
46.
LazerD.KennedyR.KingG.VespignaniA. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203–41205. https://doi.org/10.1126/science.1248506
47.
LazerD.PentlandA.AdamicL.AralS.BarabasiA. L.BrewerD.ChristakisN.ContractorN.FowlerJ.GutmannM.JebaraT.KingG.MacyM.RoyD.Van AlstyneM. (2009). Social science. Computational social science. Science, 323(5915), 721–4723. https://doi.org/10.1126/science.1167742
48.
LazerD. M.PentlandA.WattsD. J.AralS.AtheyS.ContractorN.FreelonD.Gonzalez-BailonS.KingG.MargettsH.NelsonA.SalganikM. J.StrohmaierM.VespignaniA.WagnerC. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1126/science.aaz8170
49.
LevyN.PisarevskayaA.ScholtenP. (2020). Between fragmentation and institutionalisation: The rise of migration studies as a research field. Comparative Migration Studies, 8(1), 24. https://doi.org/10.1186/s40878-020-00180-7
50.
LiuP.XiaH. (2015). Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics, 103(1), 101–134. https://doi.org/10.1007/s11192-014-1525-y
51.
MacInnisD. J.FolkesV. S. (2010). The disciplinary status of consumer behavior: A sociology of science perspective on key controversies. Journal of Consumer Research, 36(6), 899–914. https://doi.org/10.1086/644610
52.
MansillaV. B. (2010). Learning to synthesize: The development of interdisciplinarity understanding. In FrodemanR. (Ed.), The Oxford handbook of interdisciplinarity (pp. 288–306). New York: Oxford University Press.
MillerR. C. (1982). Varieties of interdisciplinary approaches in the social sciences. Issues in Integrative Studies, 1, 1–37.
55.
MillerR. C. (2020). Interdisciplinarity: Its meaning and consequences. Oxford Research Encyclopedia of International Studies, Oxford University Press.
56.
MoodyJ. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American sociological review, 69(2), 213–238. https://doi.org/10.1177/000312240406900204
57.
National Research Council (2014). Convergence: Facilitating transdisciplinary integration of life sciences, physical sciences, engineering, and beyond. National Academies Press.
58.
NewellW. H.GagnonP. (2013). The state of the field: Interdisciplinary theory. Issues in Interdisciplinary Studies, 31(2013), 22–43. http://hdl.handle.net/10323/4478.
59.
NewmanM. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409. https://doi.org/10.1515/9781400841356.221
60.
NewmanM. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582. https://doi.org/10.1073/pnas.0601602103
61.
ObermeyerZ.PowersB.VogeliC.MullainathanS. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
62.
Perianes-RodriguezA.WaltmanL.Van EckN. J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178–1195. https://doi.org/10.1016/j.joi.2016.10.006
63.
PetrieH. G. (1976). Do you see what I see? The epistemology of interdisciplinary inquiry. Educational Researcher, 5(2), 9–15. https://doi.org/10.2307/1174278
64.
PiagetJ. (1972). The epistemology of interdisciplinary relationships. In ApostelL.BergerG.BriggsA.MichaudG. (Eds.), Interdisciplinarity: Problems of teaching and research in universities (pp. 127–140). Organization for Economic Cooperation and Development (OECD).
PowellW. W.WhiteD. R.KoputK. W.Owen-SmithJ. (2005). Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology, 110(4), 1132–1205. https://doi.org/10.1086/421508
67.
RhotenD. (2004). Interdisciplinary research: Trend or transition. Items and Issues, 5(1–2), 6–11.
68.
Russell NeumanW.GuggenheimL.Mo JangS.BaeS. Y. (2014). The dynamics of public attention: Agenda-setting theory meets big data. Journal of Communication, 64(2), 193–214. https://doi.org/10.1111/jcom.12088
69.
SarewitzD. (2010). Against holism. In FrodemanR. (Ed.), The Oxford handbook of interdisciplinarity (pp. 65–75). Oxford University Press.
70.
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(1), 6–13. https://doi.org/10.1177/0002716215572084
71.
ShenC. W.HoJ. T. (2020). Technology-enhanced learning in higher education: A bibliometric analysis with latent semantic approach. Computers in Human Behavior, 104, 106177. https://doi.org/10.1016/j.chb.2019.106177
72.
ShwedU.BearmanP. S. (2010). The temporal structure of scientific consensus formation. American sociological review, 75(6), 817–840. https://doi.org/10.1177/0003122410388488
73.
SongH.EberlJ. M.EiseleO. (2020). Less fragmented than we thought? toward clarification of a subdisciplinary linkage in communication science, 2010–2019. Journal of Communication, 70(3), 310–334. https://doi.org/10.1093/joc/jqaa009
TheocharisY.JungherrA. (2020). Computational social science and the study of political communication. Political Communication, 36(1–2), 1–22. https://doi.org/10.1080/10584609.2020.1833121
76.
van AtteveldtW.PengT. Q. (2018). When communication meets computation: Opportunities, challenges, and pitfalls in computational communication science. Communication Methods and Measures, 12(2–3), 81–92. https://doi.org/10.1080/19312458.2018.1458084
77.
VargaA. (2019). Shorter distances between papers over time are due to more cross-field references and increased citation rate to higher-impact papers. Proceedings of the National Academy of Sciences, 116(44), 22094–22099. https://doi.org/10.1073/pnas.1905819116
78.
VošnerH. B.KokolP.BobekS.ŽeleznikD.ZavršnikJ. (2016). A bibliometric retrospective of the journal computers in human behavior (1991–2015). Computers in Human Behavior, 65, 46–58. https://doi.org/10.1016/j.chb.2016.08.026.
79.
WagnerC. S.RoessnerJ. D.BobbK.KleinJ. T.BoyackK. W.KeytonJ.RafolsI.BörnerK. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), 14-26. https://doi.org/10.1016/j.joi.2010.06.004
80.
WallachH. (2018). Computational social science≠ computer science+ social data. Communications of the ACM, 61(3), 42–44. https://doi.org/10.1145/3132698
81.
WangX.ChenL.ShiJ. (2021). Who sets the agenda? The dynamic agenda setting of the wildlife issue on social media. Environmental Communication. https://doi.org/10.1080/17524032.2021.1901760
82.
WassermanS.FaustK. (1994). Social network analysis: Methods and applications. Cambridge University Press.
83.
WattsD. (2016). Computational Social Science: Exciting Progress and Future Challenges. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (p. 419). Association for Computing Machinery. https://doi.org/10.1145/2939672.2945366
84.
WattsD. J.StrogatzS. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918
85.
WeingartP. (2010). A short history of knowledge formations. In FrodemanR. (Ed.), The Oxford handbook of interdisciplinarity (pp. 3–14). Oxford University Press.
