Participation takes time in both synchronous and asynchronous communication. To encourage participation, most scholars suggested strategies to change participants’ motivation to adjust the way how they distribute their time resources. We, instead, view time as an evolving environment with specific temporal norms. This study employed a multi-level event history model to explore what factors affect response behaviors in an online policy discussion forum. By proposing four time-constant individual post factors and four time-varying environment factors, and analyzing 1798 posts in the forum, the study found that both individual post and environment factors are significant predictors of online interactions in different time phases. Some interaction effects between individual post and environment factors were also found. Rather than merely focusing on participants’ resource to take part in online discussions, we suggest that future studies should examine how contextual constraints that change over time can influence response behaviors.
BarberB (1984) Strong Democracy: Participatory Politics for a New Age. Berkeley, CA: University of California Press.
2.
BeckerABDalrympleKEBrossardD, et al. (2010) Getting citizens involved: how controversial policy debates stimulate issue participation during a political campaign. International Journal of Public Opinion Research22(2): 181–203.
3.
BergeZ (1997) Characteristics of online teaching in post-secondary, formal education. Educational Technology37(3): 35–47.
4.
BrandtzaegPBLüdersM (2018) Time collapse in social media: extending the context collapse. Social Media + Society4. DOI: 10.1177/2056305118763349
5.
CappellaNJPriceVNirL (2002) Argument repertoire as a reliable and valid measure of opinion quality: electronic dialogue during campaign 2000. Political Communication19(1): 73–93.
6.
ChenBResendesMChaiCS, et al. (2017) Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interactive Learning Environments25(2): 162–175.
7.
ChenGChiuMM (2008) Online discussion processes: effects of earlier messages’ evaluations, knowledge content, social cues and personal information on later messages. Computers & Education50(3): 678–692.
8.
ChmielASienkiewiczJThelwallM, et al. (2011a). Collective emotions online and their influence on community life. PLoS ONE6(7): e22207.
9.
ChmielASobkowiczPSienkiewiczJ, et al. (2011b). Negative emotions boost user activity at BBC forum. Physica A Statistical Mechanics & Its Applications390(16): 2936–2944.
10.
ChoiSYangSWChenW (2018) Longitudinal change of an online political discussion forum: antecedents of discussion network size and evolution. Journal of Computer-Mediated Communication23(5): 260–277.
11.
ClarkJAFesslerDMT (2015) The role of disgust in norms, and of norms in disgust research: why liberals shouldn’t be morally disgusted by moral disgust. Topoi34(2): 483–498.
12.
De NooyW (2011) Networks of action and events over time. A multilevel discrete-time event history model for longitudinal network data. Social Networks33: 31–40.
13.
DeonnaJATeroniF (2017) Getting bodily feelings into emotional experience in the right way. Emotion Review9(1): 55–63.
14.
DringusLPEllisT (2010) Temporal transitions in participation flow in an asynchronous discussion forum. Computers & Education54(2): 340–349.
15.
DurringtonVAYuC (2004) It’s the same only different: the effect the discussion moderator has on student participation in online class discussions. Quarterly Review of Distance Education5(2): 89–100.
16.
EndersCKTofighiD (2007) Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological Methods12(2): 121–138.
17.
FriessDEildersC (2015) A systematic review of online deliberation research. Policy & Internet7(3): 319–339.
18.
GibbsWSimpsonLDBernasRS (2008) An analysis of temporal norms in online discussions. International Journal of Instructional Media35(1): 63–75.
19.
HallET (1983) The Dance of Life: The Other Dimension of Time. New York: Anchor Books.
20.
HesseBWWernerCMAltmanI (1988) Temporal aspects of computer-mediated communication. Computers in Human Behavior4(2): 147–165.
21.
HewittJ (2005) Toward an understanding of how threads die in asynchronous computer conferences. Journal of the Learning Sciences14(4): 567–589.
22.
HewittJTeplovsC (1999) An analysis of growth patterns in computer conferencing threads. In: Proceedings of the 1999 conference on computer support for collaborative learning. Palo Alto, CA: Stanford University. Available at: https://dl.acm.org/citation.cfm?id=1150240
23.
HowlettMGoetzKH (2014) Introduction: time, temporality and timescapes in administration and policy. International Review of Administrative Sciences80(3): 477–492.
24.
IrvinRAStansburyJ (2004) Citizen participation in decision making: is it worth the effort?Public Administration Review64(1): 55–65.
25.
JeongAC (2004) The combined effects of response time and message content on growth patterns of discussion threads in computer-supported collaborative argumentation. Journal of Distance Education19(1): 36–53.
26.
JeongACFrazierS (2008) How day of posting affects level of critical discourse in asynchronous discussions and computer-supported collaborative argumentation. British Journal of Educational Technology39(5): 875–887.
LeeHHMVan DolenW (2015) Creative participation: collective sentiment in online co-creation communities. Information & Management52(8): 951–964.
29.
LefebvreH (2004) Rhythmanalysis: Space, Time and Everyday Life (trans. EldenSMooreG). London: Continuum.
30.
MacfadyenLPDawsonS (2010) Mining LMS data to develop an “early warning system” for educators: a proof of concept. Computers & Education54(2): 588–599.
31.
MajchrzakAMalhotraA (2016) Effect of knowledge-sharing trajectories on innovative outcomes in temporary online crowds. Information Systems Research27(4): 685–703.
32.
MedagliaRYangY (2017) Online public deliberation in china: evolution of interaction patterns and network homophily in the tianya discussion forum. Information, Communication & Society20(5): 733–753.
33.
MenonSZhangW (2014) A value-based design approach for online deliberation. In: Proceedings of the 2014 conference for E-democracy and open government Asia, 4 December, pp. 81–96.
34.
MeyerKA (2003) Face-to-face versus threaded discussions: the role of time and higher-order thinking. Journal of Asynchronous Learning Networks7(3): 55–65.
35.
MoyPGastilJ (2006) Predicting deliberative conversation: the impact of discussion networks, media use, and political cognitions. Political Communication23(4): 443–460.
36.
NeshkovaMGuoD (2012) Public participation and organizational performance: evidence from state agencies. Journal of Public Administration Research and Theory22(2): 267–288.
37.
PerraultSTZhangW (2019) Effects of moderation and opinion heterogeneity on attitude towards the online deliberation experience. In: Proceedings of the 2019 CHI conference on human factors in computing systems, (paper no.17), 18April2019. ACM.
38.
PhangCWKankanhalliAHuangL (2014) Drivers of quantity and quality of participation in online policy deliberation forums. Journal of Management Information Systems31(3): 172–212.
39.
PondP (2016) Twitter time: a temporal analysis of tweet streams during televised political debate. Television & New Media17(2): 142–158.
40.
QuintelierE (2010) The effect of schools on political participation: a multilevel logistic analysis. Research Papers in Education25(2): 137–154.
41.
SorokaSMcAdamsS (2015) News, politics, and negativity. Political Communication32(1): 1–22.
TatsumiTNakazawaT (2017) Why does no one respond? An analysis of threads with no responses in online asynchronous discussions. In: International conference on deliberation and decision making, Singapore, 23–24June.
44.
WaltherJBTidwellLC (1995) Nonverbal cues in computer-mediated communication, and the effect of chronemics on relational communication. Journal of Organizational Computing5(4): 355–378.
45.
ZhangWCaoXTranMN (2013) The structural features and the deliberative quality of online discussions. Telematics and Informatics30(2): 74–86.
46.
ZhangWSoonC (2017) The main and spillover effects of online deliberation: changes in opinions, informational media use and political activities. In: Proceedings of the 2017 conference for E-democracy and open government (CeDEM), 17 May 2017, pp. 217–226. IEEE.
