This study investigates the Facebook posting behaviour of 922 posting users over a time span of seven years (from 2007 to 2014), using an innovative combination of survey data and private profile feed post counts obtained through the Facebook Application Programming Interface (API) prior to the changes in 2015. A digital inequality lens is applied to study the effect of socio-demographic characteristics as well as time on posting behaviour. The findings indicate differences, for example in terms of gender and age, but some of this inequality is becoming smaller over time. The data set also shows inequality in the poster ratio in different age groups. Across all the demographic groups, the results show an increase in posting frequency in the time period observed, and limited evidence is found that young age groups have posted less on Facebook in more recent years.
Baker, R., Blumberg, S. J., Brick, J. M., Couper, M. P., Courtright, M., Dennis, J. M., … Zahs, D. (2010). AAPOR report on online panels. Public Opinion Quarterly, 74(4): 711-781.
Baumer, E. P. S., Adams, P., Khovanskaya, V. D., Liao, T. C., Smith, M. E., Schwanda Sosik, V. & Williams, K. (2013). Limiting, leaving, and (re)lapsing: An exploration of Facebook non-use practices and experiences. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 3257-3266). New York, NY: ACM. https://doi.org/10.1145/2470654.2466446
Bechmann, A. (2019). Data as humans: Representation, accountability, and equality in big data and machine learning. In Power and rights in the online domain. Cambridge, MA: MIT Press.
Bechmann, A. & Lomborg, S. (2013). Mapping actor roles in social media: Different perspectives on value creation in theories of user participation. New Media & Society, 15(5): 765-781. https://doi.org/10.1177/1461444812462853
Bernstein, M. S., Bakshy, E., Burke, M. & Karrer, B. (2013). Quantifying the invisible audience in social networks. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 21-30). New York, NY: ACM. https://doi.org/10.1145/2470654.2470658
Blank, G. & Lutz, C. (2017). Representativeness of social media in Great Britain: Investigating Facebook, LinkedIn, Twitter, Pinterest, Google+, and Instagram. American Behavioral Scientist, 61(7): 741-756. https://doi.org/10.1177/0002764217717559
Bowker, G. C. (2014). Big data, big questions: The theory/data thing. International Journal of Communication, 8(0): 5.
Burke, M., Marlow, C. & Lento, T. (2010). Social network activity and social well-being. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1909-1912). New York, NY: ACM. https://doi.org/10.1145/1753326.1753613
Cameron, A. C. & Trivedi, P. K. (2010). Microeconometrics using Stata: Revised edition (2nd edition). College Station, TX: Stata Press.
Correa, T. (2010). The participation divide among “online experts”: Experience, skills and psychological factors as predictors of college students’ web content creation. Journal of Computer-Mediated Communication, 16(1): 71-92. https://doi.org/10.1111/j.1083-6101.2010.01532.x
Deursen, A. J. A. M. van & Dijk, J. A. G. M. van. (2015). Internet skill levels increase, but gaps widen: A longitudinal cross-sectional analysis (2010-2013) among the Dutch population. Information, Communication & Society, 18(7): 782-797. https://doi.org/10.1080/1369118X.2014.994544
Hargittai, E. (2015). Is bigger always better? Potential biases of big data derived from social network sites. ANNALS of the American Academy of Political and Social Science, 659(1): 63-76. https://doi.org/10.1177/0002716215570866
Hargittai, E. & Walejko, G. (2008). The participation divide: Content creation and sharing in the digital age. Information Communication and Society, 11(2): 239-256. https://doi.org/10.1080/13691180801946150
Hausmann, R., Tyson, L. D. & Zahidi, S. (2012). The global gender gap report 2012. Geneva: World Economic Forum.
Hoffmann, C. P., Lutz, C. & Meckel, M. (2015). Content creation on the Internet: A social cognitive perspective on the participation divide. Information, Communication & Society, 18(6): 696-716. https://doi.org/10.1080/1369118X.2014.991343
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage.
Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A. & Christakis, N. (2008). Tastes, ties, and time: A new social network dataset using Facebook.com. Social Networks, 30(4): 330-342. https://doi.org/10.1016/j.socnet.2008.07.002
McAndrew, F. T. & Jeong, H. S. (2012). Who does what on Facebook? Age, sex, and relationship status as predictors of Facebook use. Computers in Human Behavior, 28(6): 2359-2365. https://doi.org/10.1016/j.chb.2012.07.007
Nonnecke, B. & Preece, J. (2000). Lurker demographics: Counting the silent. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 73-80). New York, NY: ACM. https://doi.org/10.1145/332040.332409