Evidence of Online Performance Deterioration in User Sessions on Reddit

This article presents evidence of performance deterioration in online user sessions quantified by studying a massive dataset containing over 55 million comments posted on Reddit in April 2015. After segmenting the sessions (i.e., periods of activity without a prolonged break) depending on their intensity (i.e., how many posts users produced during sessions), we observe a general decrease in the quality of comments produced by users over the course of sessions. We propose mixed-effects models that capture the impact of session intensity on comments, including their length, quality, and the responses they generate from the community. Our findings suggest performance deterioration: Sessions of increasing intensity are associated with the production of shorter, progressively less complex comments, which receive declining quality scores (as rated by other users), and are less and less engaging (i.e., they attract fewer responses). Our contribution evokes a connection between cognitive and attention dynamics and the usage of online social peer production platforms, specifically the effects of deterioration of user performance.

[1]  R. Baumeister,et al.  Self-regulation and depletion of limited resources: does self-control resemble a muscle? , 2000, Psychological bulletin.

[2]  G. Borghini,et al.  Neuroscience and Biobehavioral Reviews , 2022 .

[3]  Maro G. Machizawa,et al.  Neural activity predicts individual differences in visual working memory capacity , 2004, Nature.

[4]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[5]  R. Marois,et al.  Capacity limits of information processing in the brain , 2005, Trends in Cognitive Sciences.

[6]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.

[7]  K. R. Ridderinkhof,et al.  Impaired cognitive control and reduced cingulate activity during mental fatigue. , 2005, Brain research. Cognitive brain research.

[8]  Fabian Flöck,et al.  Evolution of reddit: from the front page of the internet to a self-referential community? , 2014, WWW.

[9]  Andrea Tagarelli,et al.  Online popularity and topical interests through the lens of instagram , 2014, HT.

[10]  R. Baayen,et al.  Mixed-effects modeling with crossed random effects for subjects and items , 2008 .

[11]  R. P. Fishburne,et al.  Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel , 1975 .

[12]  James A. Kole,et al.  Effects of prolonged work on data entry speed and accuracy. , 2004, Journal of experimental psychology. Applied.

[13]  Krishna P. Gummadi,et al.  Quantifying Information Overload in Social Media and Its Impact on Social Contagions , 2014, ICWSM.

[14]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[15]  Robin I. M. Dunbar.  Neocortex size as a constraint on group size in primates , 1992 .

[16]  J L Kenemans,et al.  Mental Fatigue : Costs and Benefits , 2005 .

[17]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[18]  Angela L. Duckworth,et al.  An opportunity cost model of subjective effort and task performance. , 2013, The Behavioral and brain sciences.

[19]  E. Soetens,et al.  Psychophysiological investigation of vigilance decrement: Boredom or cognitive fatigue? , 2008, Physiology & Behavior.

[20]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[21]  F. Gino,et al.  Cognitive fatigue influences students’ performance on standardized tests , 2016, Proceedings of the National Academy of Sciences.

[22]  A. Gelman.  Analysis of variance: Why it is more important than ever? , 2005, math/0504499.

[23]  George R. Klare,et al.  The measurement of readability , 1963 .

[24]  Esteban Moro Egido,et al.  Time as a limited resource: Communication Strategy in Mobile Phone Networks , 2013, Soc. Networks.

[25]  Alessandro Vespignani,et al.  Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number , 2011, PloS one.

[26]  Kristina Lerman,et al.  Evolution of Conversations in the Age of Email Overload , 2015, WWW.

[27]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[28]  Sean J. Taylor,et al.  Social Influence Bias: A Randomized Experiment , 2013, Science.

[29]  John A. Detre,et al.  Imaging brain fatigue from sustained mental workload: An ASL perfusion study of the time-on-task effect , 2010, NeuroImage.

[30]  Aaron Halfaker,et al.  Using edit sessions to measure participation in wikipedia , 2013, CSCW.

[31]  Robert B. Allen,et al.  User Models: Theory, Method, and Practice , 1990, Int. J. Man Mach. Stud..

[32]  Kristina Lerman,et al.  The Simple Rules of Social Contagion , 2013, Scientific Reports.

[33]  Noelle M. Nelson,et al.  Making choices impairs subsequent self-control: a limited-resource account of decision making, self-regulation, and active initiative. , 2008, Journal of personality and social psychology.

[34]  Aaron Halfaker,et al.  User Session Identification Based on Strong Regularities in Inter-activity Time , 2014, WWW.

[35]  Filippo Menczer,et al.  The production of information in the attention economy , 2014, Scientific Reports.

[36]  András Kornai,et al.  Dynamics of Conflicts in Wikipedia , 2012, PloS one.

[37]  Kristina Lerman,et al.  How Visibility and Divided Attention Constrain Social Contagion , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[38]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.