Abstract
The design of online platforms is both critically important and challenging, as any changes may lead to unintended consequences, and it can be hard to predict how users will react. Here we conduct a case study of a particularly important real-world platform design change: Twitter's decision to double the character limit from 140 to 280 characters to soothe users' need to "cram" or "squeeze" their tweets, informed by modeling of historical user behavior. In our analysis, we contrast Twitter's anticipated pre-intervention predictions about user behavior with actual post-intervention user behavior: Did the platform design change lead to the intended user behavior shifts, or did a gap between anticipated and actual behavior emerge? Did different user groups react differently? We find that even though users do not "cram" as much under 280 characters as they used to under 140 characters, emergent "cramming" at the new limit seems to not have been taken into account when designing the platform change. Furthermore, investigating textual features, we find that, although post-intervention "crammed" tweets are longer, their syntactic and semantic characteristics remain similar and indicative of "squeezing". Applying the same approach as Twitter policy-makers, we create updated counterfactual estimates and find that the character limit would need to be increased further to reduce cramming that re-emerged at the new limit. We contribute to the rich literature studying online user behavior with an empirical study that reveals a dynamic interaction between platform design and user behavior, with immediate policy and practical implications for the design of socio-technical systems.
- Tony Ahn, Seewon Ryu, and Ingoo Han. 2007. The impact of Web quality and playfulness on user acceptance of online retailing. Information & management, Vol. 44, 3 (2007), 263--275.Google Scholar
- Yoav Artzi, Patrick Pantel, and Michael Gamon. 2012. Predicting responses to microblog posts. In Proc. Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT).Google Scholar
- Eytan Bakshy, Jake M Hofman, Winter A Mason, and Duncan J Watts. 2011. Everyone's an influencer: Quantifying influence on Twitter. In Proc. ACM International Conference on Web Search and Data Mining (WSDM).Google ScholarDigital Library
- Samuel Barbosa, Dan Cosley, Amit Sharma, and Roberto M Cesar Jr. 2016. Averaging gone wrong: Using time-aware analyses to better understand behavior. In Proceedings of the 25th International Conference on World Wide Web (TheWebConf).Google ScholarDigital Library
- Elena Belavina, Simone Marinesi, and Gerry Tsoukalas. 2020. Rethinking crowdfunding platform design: mechanisms to deter misconduct and improve efficiency. Management Science, Vol. 66, 11 (2020), 4980--4997.Google ScholarDigital Library
- Jonah Berger and Katherine L Milkman. 2012. What makes online content viral? Journal of Marketing Research, Vol. 49, 2 (2012), 192--205.Google ScholarCross Ref
- Arnout B Boot, Erik Tjong Kim Sang, Katinka Dijkstra, and Rolf A Zwaan. 2019. How character limit affects language usage in tweets. Palgrave Communications, Vol. 5, 1 (2019), 1--13.Google ScholarCross Ref
- Danah Boyd, Scott Golder, and Gilad Lotan. 2010. Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In 2010 43rd Hawaii International Conference on System Sciences. 1--10.Google ScholarDigital Library
- Yann Bramoulle and Lorenzo Ductor. 2018. Title length. Journal of Economic Behavior & Organization, Vol. 150 (2018), 311--324.Google ScholarCross Ref
- Daniel Buschek, Alexander De Luca, and Florian Alt. 2015. Improving accuracy, applicability and usability of keystroke biometrics on mobile touchscreen devices. In Proc ACM Conference on Human Factors in Computing Systems (CHI).Google ScholarDigital Library
- Damon Centola, Joshua Becker, Devon Brackbill, and Andrea Baronchelli. 2018. Experimental evidence for tipping points in social convention. Science, Vol. 360, 6393 (2018), 1116--1119.Google Scholar
- Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy Bruckman, Cliff Lampe, Jacob Eisenstein, and Eric Gilbert. 2018. The Internet's Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso, and Macro Scales. Proc. ACM Hum.-Comput. Interact. (CSCW), Vol. 2 (2018).Google ScholarDigital Library
- Hsia-Ching Chang. 2010. A new perspective on Twitter hashtag use: Diffusion of innovation theory. Proceedings of the American Society for Information Science and Technology, Vol. 47, 1 (2010), 1--4.Google Scholar
- Nikan Chavoshi, Hossein Hamooni, and Abdullah Mueen. 2016. Debot: Twitter bot detection via warped correlation.. In ICDM. 817--822.Google Scholar
- Morgane Ciot, Morgan Sonderegger, and Derek Ruths. 2013. Gender inference of Twitter users in non-English contexts. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP).Google Scholar
- Isobelle Clarke and Jack Grieve. 2019. Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018. PloS one, Vol. 14, 9 (2019), e0222062.Google ScholarCross Ref
- Christophe Coupé, Yoon Mi Oh, Dan Dediu, and Francc ois Pellegrino. 2019. Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche. Science Advances, Vol. 5, 9 (2019), eaaw2594.Google Scholar
- Evandro Cunha, Gabriel Magno, Giovanni Comarela, Virgilio Almeida, Marcos Andre Goncalves, and Fabricio Benevenuto. 2011. Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. In Proceedings of the Workshop on Language in Social Media (LSM 2011). 58--65.Google Scholar
- Michael Cusumano. 2010. The evolution of platform thinking. Commun. ACM, Vol. 53, 1 (2010), 32--34.Google ScholarDigital Library
- Munmun De Choudhury and Sushovan De. 2014. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. In Proc. International AAAI Conference on Weblogs and Social Media (ICWSM).Google ScholarCross Ref
- Stefano DellaVigna and Devin Pope. 2018. Predicting experimental results: who knows what? Journal of Political Economy, Vol. 126, 6 (2018), 2410--2456.Google ScholarCross Ref
- Gabriel Doyle, Dan Yurovsky, and Michael C Frank. 2016. A robust framework for estimating linguistic alignment in Twitter conversations. In Proceedings of the 25th International Conference on World Wide Web (TheWebConf).Google ScholarDigital Library
- Jacob Eisenstein. 2013. What to do about bad language on the internet. In Proc. Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT).Google Scholar
- Edwin J Elton, Martin J Gruber, and Christopher R Blake. 1996. Survivor bias and mutual fund performance. The Review of Financial Studies, Vol. 9, 4 (1996), 1097--1120.Google ScholarCross Ref
- Lucie Flekova, Daniel Preoct iuc-Pietro, and Lyle Ungar. 2016. Exploring stylistic variation with age and income on twitter. In Proc. of the 54th Annual Meeting of the Association for Computational Linguistics (ACL).Google ScholarCross Ref
- Boris Fritscher and Yves Pigneur. 2009. Supporting business model modelling: A compromise between creativity and constraints. In Proc. International Workshop on Task Models and Diagrams.Google Scholar
- Kiran Garimella, Ingmar Weber, and Munmun De Choudhury. 2016. Quote RTs on Twitter: usage of the new feature for political discourse. In Proceedings of the 8th ACM Conference on Web Science (WebSci).Google ScholarDigital Library
- Kristina Gligorić, Ashton Anderson, and Robert West. 2018. How constraints affect content: The case of Twitter's switch from 140 to 280 characters. In Proc. International AAAI Conference on Web and Social Media (ICWSM).Google ScholarCross Ref
- Kristina Gligorić, Ashton Anderson, and Robert West. 2019. Causal Effects of Brevity on Style and Success in Social Media. Proc. ACM Hum.-Comput. Interact. (CSCW), Vol. 3 (Nov. 2019).Google ScholarDigital Library
- Kristina Gligorić, George Lifchits, Robert West, and Ashton Anderson. 2021. Linguistic effects on news headline success: Evidence from thousands of online field experiments (Registered Report Protocol). Plos one, Vol. 16, 9 (2021), e0257091.Google ScholarCross Ref
- Kristina Gligorić, Manoel Horta Ribeiro, Martin Müller, Olesia Altunina, Maxime Peyrard, Marcel Salathé, Giovanni Colavizza, and Robert West. 2020. Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis. arXiv preprint arXiv:2008.08364 (2020).Google Scholar
- Marco Guerini, Carlo Strapparava, and Gözde Özbal. 2011. Exploring text virality in social networks. In Proc. International AAAI Conference on Weblogs and Social Media (ICWSM).Google Scholar
- Daniel Halpern and Jennifer Gibbs. 2013. Social media as a catalyst for online deliberation? Exploring the affordances of Facebook and YouTube for political expression. Computers in Human Behavior, Vol. 29, 3 (2013), 1159--1168.Google ScholarDigital Library
- Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters. 2016. Strategic classification. In Proceedings of the 2016 ACM conference on innovations in theoretical computer science. 111--122.Google ScholarDigital Library
- Beth A Hennessey. 1989. The effect of extrinsic constraints on children's creativity while using a computer. Creativity Research Journal, Vol. 2, 3 (1989), 151--168.Google ScholarCross Ref
- Jake M Hofman, Duncan J Watts, Susan Athey, Filiz Garip, Thomas L Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J Salganik, Simine Vazire, et al. 2021. Integrating explanation and prediction in computational social science. Nature, Vol. 595, 7866 (2021), 181--188.Google Scholar
- Yuheng Hu, Kartik Talamadupula, and Subbarao Kambhampati. 2013. Dude, srsly?: The surprisingly formal nature of Twitter's language. In Proc. International AAAI Conference on Weblogs and Social Media (ICWSM).Google Scholar
- Ferenc Huszár, Sofia Ira Ktena, Conor O'Brien, Luca Belli, Andrew Schlaikjer, and Moritz Hardt. 2022. Algorithmic amplification of politics on Twitter. Proceedings of the National Academy of Sciences (PNAS), Vol. 119, 1 (2022).Google Scholar
- Ihara Ikuhiro. 2017. Our discovery of cramming. https://web.archive.org/web/20220428070306/https://blog.twitter.com/engineering/en_us/topics/insights/2017/Our-Discovery-of-Cramming.Google Scholar
- Kokil Jaidka, Alvin Zhou, and Yphtach Lelkes. 2019. Brevity is the soul of Twitter: The constraint affordance and political discussion. Journal of Communication, Vol. 69, 4 (2019), 345--372.Google ScholarCross Ref
- Shagun Jhaver, Christian Boylston, Diyi Yang, and Amy Bruckman. 2021. Evaluating the Effectiveness of Deplatforming as a Moderation Strategy on Twitter. Proc. ACM Hum.-Comput. Interact. (CSCW), Vol. 5 (oct 2021).Google ScholarDigital Library
- Huiyuan Jin and Haitao Liu. 2017. How will text size influence the length of its linguistic constituents? Poznan Studies in Contemporary Linguistics, Vol. 53, 2 (2017), 197--225.Google ScholarCross Ref
- Caneel K Joyce. 2009. The Blank Page: Effects of Constraint on Creativity. PhD thesis, UC Berkeley.Google Scholar
- Farshad Kooti, Winter A Mason, Krishna P Gummadi, and Meeyoung Cha. 2012a. Predicting emerging social conventions in online social networks. In Proc. of the ACM International Conference on Information and Knowledge Management (CIKM). 445--454.Google ScholarDigital Library
- Farshad Kooti, Haeryun Yang, Meeyoung Cha, P Krishna Gummadi, and Winter A Mason. 2012b. The Emergence of Conventions in Online Social Networks.. In Proc. International AAAI Conference on Weblogs and Social Media (ICWSM).Google Scholar
- Sneha Kudugunta and Emilio Ferrara. 2018. Deep neural networks for bot detection. Information Sciences, Vol. 467 (2018), 312--322.Google ScholarCross Ref
- Nevin Laib. 1990. Conciseness and amplification. College Composition and Communication, Vol. 41, 4 (1990), 443--459.Google ScholarCross Ref
- Sotiris Lamprinidis, Daniel Hardt, and Dirk Hovy. 2018. Predicting news headline popularity with syntactic and semantic knowledge using multi-task learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP).Google ScholarCross Ref
- Paul Levinson. 2011. The long story about the short medium: Twitter as a communication medium in historical, present, and future context. Journal of Communication Research, Vol. 48 (2011), 7--28.Google ScholarCross Ref
- Momin M Malik and Jürgen Pfeffer. 2016. Identifying platform effects in social media data. In Proc. International AAAI Conference on Weblogs and Social Media (ICWSM).Google ScholarCross Ref
- Travis Martin, Jake M Hofman, Amit Sharma, Ashton Anderson, and Duncan J Watts. 2016. Exploring limits to prediction in complex social systems. In Proceedings of the 25th International Conference on World Wide Web (TheWebConf).Google ScholarDigital Library
- Marshall McLuhan. 1964. The extensions of man. New York (1964).Google Scholar
- J McPhee. 2015. Omission: Choosing what to leave out. https://web.archive.org/web/20220710175229/https://www.newyorker.com/magazine/2015/09/14/omission. The New Yorker (2015).Google Scholar
- Katherine L Milkman, Dena Gromet, Hung Ho, Joseph S Kay, Timothy W Lee, Pepi Pandiloski, Yeji Park, Aneesh Rai, Max Bazerman, John Beshears, et al. 2021. Megastudies improve the impact of applied behavioural science. Nature, Vol. 600, 7889 (2021), 478--483.Google Scholar
- John Miller, Smitha Milli, and Moritz Hardt. 2020. Strategic classification is causal modeling in disguise. In International Conference on Machine Learning (ICML).Google Scholar
- Page C Moreau and Darren W Dahl. 2005. Designing the solution: The impact of constraints on consumers' creativity. Journal of Consumer Research, Vol. 32, 1 (2005), 13--22.Google ScholarCross Ref
- John DW Morecroft. 2015. Strategic modelling and business dynamics: A feedback systems approach. John Wiley & Sons.Google Scholar
- Fred Morstatter, Jürgen Pfeffer, Huan Liu, and Kathleen M Carley. 2013. Is the sample good enough? Comparing data from Twitter's streaming API with Twitter's Firehose. arXiv preprint arXiv:1306.5204 (2013).Google Scholar
- Dhiraj Murthy. 2012. Towards a sociological understanding of social media: Theorizing Twitter. Sociology, Vol. 46, 6 (2012), 1059--1073.Google Scholar
- Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. 2019. Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, Vol. 2 (2019), 13.Google ScholarCross Ref
- Ruth Page. 2012. The linguistics of self-branding and micro-celebrity in Twitter: The role of hashtags. Discourse & communication, Vol. 6, 2 (2012), 181--201.Google Scholar
- Ethan Pancer and Maxwell Poole. 2016. The popularity and virality of political social media: hashtags, mentions, and links predict likes and retweets of 2016 U.S. presidential nominees tweets. Social Influence, Vol. 11, 4 (2016), 259--270.Google ScholarCross Ref
- Umashanthi Pavalanathan and Jacob Eisenstein. 2016. More emojis, less:) The competition for paralinguistic function in microblog writing. First Monday (2016).Google Scholar
- Judea Pearl and Dana Mackenzie. 2018. The book of why: the new science of cause and effect. Basic books.Google Scholar
- James W Pennebaker, Roger J Booth, and Martha E Francis. 2007. LIWC2007: Linguistic inquiry and word count. Austin, Texas: liwc. net (2007).Google Scholar
- Sarah Perez. 2017. Twitter's doubling of character count from 140 to 280 had little impact on length of tweets. https://cutt.ly/gfoIaY1.Google Scholar
- Andrew Perrin and Monica Anderson. 2019. Share of US adults using social media, including Facebook, is mostly unchanged since 2018. Pew Research Center, Vol. 10 (2019).Google Scholar
- Jürgen Pfeffer, Katja Mayer, and Fred Morstatter. 2018. Tampering with Twitter's sample API. EPJ Data Science, Vol. 7, 1 (2018), 50.Google ScholarCross Ref
- Shalini Priya, Ryan Sequeira, Joydeep Chandra, and Sourav Kumar Dandapat. 2019. Where should one get news updates: Twitter or reddit. Online Social Networks and Media, Vol. 9 (2019), 17--29.Google ScholarCross Ref
- R.T. Ramos, R.B. Sassi, and J.R.C. Piqueira. 2011. Self-organized criticality and the predictability of human behavior. New Ideas in Psychology, Vol. 29, 1 (2011), 38--48.Google ScholarCross Ref
- Rimjhim and Roshni Chakraborty. 2018. Characterizing User Reactions Towards Twitter's 280 Character Limit. In Proceedings of the 10th Annual Meeting of the Forum for Information Retrieval Evaluation (FIRE) (Gandhinagar, India). 48--51.Google Scholar
- Aliza Rosen. 2017. Tweeting Made Easier. https://blog.twitter.com/official/en_us/topics/product/2017/tweetingmadeeasier.html.Google Scholar
- Aliza Rosen and Ikuhiro Ihara. 2017. Giving you more characters to express yourself. https://blog.twitter.com/official/en_us/topics/product/2017/Giving-you-more-characters-to-express-y ourself.html.Google Scholar
- Paul R Rosenbaum. 2005. Sensitivity analysis in observational studies. Encyclopedia of statistics in behavioral science (2005).Google Scholar
- Paul R Rosenbaum, PR Rosenbaum, and Briskman. 2010. Design of observational studies. Vol. 10. Springer.Google Scholar
- Matthew J Salganik, Ian Lundberg, Alexander T Kindel, Caitlin E Ahearn, Khaled Al-Ghoneim, Abdullah Almaatouq, Drew M Altschul, Jennie E Brand, Nicole Bohme Carnegie, Ryan James Compton, et al. 2020. Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences (PNAS), Vol. 117, 15 (2020), 8398--8403.Google ScholarCross Ref
- Carsten D Schultz. 2017. Proposing to your fans: Which brand post characteristics drive consumer engagement activities on social media brand pages? Electronic Commerce Research and Applications, Vol. 26 (2017), 23--34.Google ScholarDigital Library
- Indira Sen, Fabian Floeck, Katrin Weller, Bernd Weiss, and Claudia Wagner. 2019. A total error framework for digital traces of humans. arXiv preprint arXiv:1907.08228 (2019).Google Scholar
- Allison Shapp. 2014. Variation in the use of Twitter hashtags. New York University (2014), 1--44.Google Scholar
- Benjamin Shulman, Amit Sharma, and Dan Cosley. 2016. Predictability of popularity: Gaps between prediction and understanding. In Proc. International AAAI Conference on Web and Social Media (ICWSM), Vol. 10.Google Scholar
- Brenda S Sloane. 2003. Say it straight: Teaching conciseness. Teaching English in the Two Year College (2003).Google Scholar
- Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of Predictability in Human Mobility. Science, Vol. 327, 5968 (2010), 1018--1021.Google Scholar
- Briony Swire-Thompson, Joseph DeGutis, and David Lazer. 2020. Searching for the backfire effect: Measurement and design considerations. Journal of Applied Research in Memory and Cognition (2020).Google Scholar
- Karolina Sylwester and Matthew Purver. 2015. Twitter language use reflects psychological differences between democrats and republicans. PloS one, Vol. 10, 9 (2015), e0137422.Google ScholarCross Ref
- Chenhao Tan, Lillian Lee, and Bo Pang. 2014. The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter. In Proc. Annual Meeting of the Association for Computational Linguistics (ACL).Google ScholarCross Ref
- Amiel D Vardi. 2000. Brevity, conciseness, and compression in Roman poetic criticism and the text of Gellius' Noctes Atticae 19.9. 10. American Journal of Philology (2000).Google Scholar
- Claudia Wagner, Markus Strohmaier, Alexandra Olteanu, Emre Kiciman, Noshir Contractor, and Tina Eliassi-Rad. 2021. Measuring algorithmically infused societies. Nature, Vol. 595, 7866 (2021), 197--204.Google Scholar
- Timm F Wagner, Christian V Baccarella, and Kai-Ingo Voigt. 2017. Framing social media communication: Investigating the effects of brand post appeals on user interaction. European Management Journal, Vol. 35, 5 (2017), 606--616.Google ScholarCross Ref
- Shuting Ada Wang and Brad N Greenwood. 2020. Does Length Impact Engagement? Length Limits of Posts and Microblogging Behavior. Length Limits of Posts and Microblogging Behavior (February 12, 2020) (2020).Google Scholar
- Ben S Wasike. 2013. Framing News in 140 Characters: How Social Media Editors Frame the News and Interact with Audiences via Twitter. Global Media Journal: Canadian Edition, Vol. 6, 1 (2013).Google Scholar
- Barbara Wejnert. 2002. Integrating models of diffusion of innovations: A conceptual framework. Annual Review of Sociology, Vol. 28, 1 (2002), 297--326.Google ScholarCross Ref
- Peter Wikström. 2014. # srynotfunny: Communicative functions of hashtags on Twitter. SKY Journal of Linguistics, Vol. 27 (2014).Google Scholar
- Siqi Wu, Marian-Andrei Rizoiu, and Lexing Xie. 2020. Variation across Scales: Measurement Fidelity under Twitter Data Sampling. In Proc. International AAAI Conference on Web and Social Media (ICWSM), Vol. 14. 715--725.Google ScholarCross Ref
- Kai-Cheng Yang, Onur Varol, Pik-Mai Hui, and Filippo Menczer. 2020. Scalable and generalizable social bot detection through data selection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1096--1103.Google ScholarCross Ref
- Kai Zhao, Denis Khryashchev, and Huy Vo. 2021. Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability. IEEE Transactions on Knowledge and Data Engineering, Vol. 33, 6 (2021), 2723--2736.Google ScholarCross Ref
- Alvin Zhou and Sifan Xu. 2019. Remaking dialogic principles for the digital age: The role of affordances in dialogue and engagement. SocArXiv (2019).Google Scholar
Index Terms
- Anticipated versus Actual Effects of Platform Design Change: A Case Study of Twitter's Character Limit
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