Abstract
In online communities, where billions of people strive to propagate their messages, understanding how wording affects success is of primary importance. In this work, we are interested in one particularly salient aspect of wording: brevity. What is the causal effect of brevity on message success? What are the linguistic traits of brevity? When is brevity beneficial, and when is it not? Whereas most prior work has studied the effect of wording on style and success in observational setups, we conduct a controlled experiment, in which crowd workers shorten social media posts to prescribed target lengths and other crowd workers subsequently rate the original and shortened versions. This allows us to isolate the causal effect of brevity on the success of a message. We find that concise messages are on average more successful than the original messages up to a length reduction of 30--40%. The optimal reduction is on average between 10% and 20%. The observed effect is robust across different subpopulations of raters and is the strongest for raters who visit social media on a daily basis. Finally, we discover unique linguistic and content traits of brevity and correlate them with the measured probability of success in order to distinguish effective from ineffective shortening strategies. Overall, our findings are important for developing a better understanding of the effect of brevity on the success of messages in online social media.
- 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.Google Scholar
- Lars Backstrom, Eytan Bakshy, Jon M Kleinberg, Thomas M Lento, and Itamar Rosenn. 2011. Center of attention: How Facebook users allocate attention across friends. Proc. International Conference on Web and Social Media (2011).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.Google Scholar
Digital Library
- Roy F Baumeister, Ellen Bratslavsky, Catrin Finkenauer, and Kathleen D Vohs. 2001. Bad is stronger than good. Review of General Psychology, Vol. 5, 4 (2001), 323.Google Scholar
Cross Ref
- David Bawden and Lyn Robinson. 2009. The dark side of information: Overload, anxiety and other paradoxes and pathologies. Journal of Information Science, Vol. 35, 2 (2009), 180--191.Google Scholar
Digital Library
- Jonah Berger and Katherine L Milkman. 2012. What makes online content viral? Journal of Marketing Research, Vol. 49, 2 (2012), 192--205.Google Scholar
Cross Ref
- Michael S Bernstein, Greg Little, Robert C Miller, Björn Hartmann, Mark S Ackerman, David R Karger, David Crowell, and Katrina Panovich. 2015. Soylent: A word processor with a crowd inside. Commun. ACM, Vol. 58, 8 (2015), 85--94.Google Scholar
Digital Library
- Yann Bramoulle and Lorenzo Ductor. 2018. Title length. Journal of Economic Behavior & Organization, Vol. 150 (2018), 311--324.Google Scholar
Cross Ref
- Steven Burrows, Martin Potthast, and Benno Stein. 2013. Paraphrase acquisition via crowdsourcing and machine learning. ACM Transactions on Intelligent Systems and Technology, Vol. 4, 3 (2013), 43.Google Scholar
Digital Library
- Ziqiang Cao, Chengyao Chen, Wenjie Li, Sujian Li, Furu Wei, and Ming Zhou. 2016. TGSum: Build tweet guided multi-document summarization dataset. In Proc. AAAI Conference on Artificial Intelligence.Google Scholar
- Raman Chandrasekar and Bangalore Srinivas. 1997. Automatic induction of rules for text simplification. Knowledge-Based Systems, Vol. 10, 3 (1997), 183--190.Google Scholar
Digital Library
- Justin Cheng, Michael Bernstein, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. 2017. Anyone can become a troll: Causes of trolling behavior in online discussions. In Proc. ACM Conference on Computer Supported Cooperative Work and Social Computing.Google Scholar
Digital Library
- Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. A computational approach to politeness with application to social factors. Proc. Annual Meeting of the Association for Computational Linguistics (2013).Google Scholar
- Marie-Paule Daniel and Michel Denis. 2004. The production of route directions: Investigating conditions that favour conciseness in spatial discourse. Applied Cognitive Psychology, Vol. 18, 1 (2004), 57--75.Google Scholar
Cross Ref
- Christopher Dougherty. 2011. Introduction to Econometrics. Oxford University Press.Google Scholar
- Marko Dragojevic and Howard Giles. 2016. I don't like you because you're hard to understand: The role of processing fluency in the language attitudes process. Human Communication Research, Vol. 42, 3 (2016), 396--420.Google Scholar
Cross Ref
- 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.Google Scholar
- 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
- 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 Conference on Web and Social Media.Google Scholar
- Manuel Gómez-Rodríguez, Krishna P Gummadi, and Bernhard Schölkopf. 2014. Quantifying information overload in social media and its impact on social contagions. In Proc. International Conference on Web and Social Media.Google Scholar
- Marco Guerini, Carlo Strapparava, and Gözde Özbal. 2011. Exploring text virality in social networks. In Proc. International Conference on Web and Social Media.Google Scholar
- 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 Scholar
Cross Ref
- Yuheng Hu, Kartik Talamadupula, and Subbarao Kambhampati. 2013. Dude, srsly?: The surprisingly formal nature of Twitter's language. In Proc. International Conference on Web and Social Media.Google Scholar
- Bo Jiang, Nidhi Hegde, Laurent Massoulié, and Don Towsley. 2013. How to optimally allocate your budget of attention in social networks. In Proc. IEEE International Conference on Computer Communications.Google Scholar
Cross Ref
- Quentin Jones, Gilad Ravid, and Sheizaf Rafaeli. 2004. Information overload and the message dynamics of online interaction spaces: A theoretical model and empirical exploration. Information Systems Research, Vol. 15, 2 (2004), 194--210.Google Scholar
Digital Library
- Caneel K Joyce. 2009. The Blank Page: Effects of Constraint on Creativity .PhD thesis, UC Berkeley.Google Scholar
- Daniel Jurafsky, Alan Bell, Michelle Gregory, and William D Raymond. 2001. Probabilistic relations between words: Evidence from reduction in lexical production. Typological Studies in Language, Vol. 45 (2001), 229--254.Google Scholar
Cross Ref
- Chiranjeev Kohli, Sunil Thomas, and Rajneesh Suri. 2013. Are you in good hands? Slogan recall: What really matters. Journal of Advertising Research, Vol. 53, 1 (2013), 31--42.Google Scholar
Cross Ref
- Nevin Laib. 1990. Conciseness and amplification. College Composition and Communication, Vol. 41, 4 (1990), 443--459.Google Scholar
Cross Ref
- Sotiris Lamprinidis, Daniel Hardt, and Dirk Hovy. 2018. Predicting news headline popularity with syntactic and semantic knowledge using multi-task learning. In Proc. Conference on Empirical Methods in Natural Language Processing.Google Scholar
Cross Ref
- Walter S Lasecki, Luz Rello, and Jeffrey P Bigham. 2015. Measuring text simplification with the crowd. In Proc. ACM Web for All Conference.Google Scholar
Digital Library
- 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 Scholar
Cross Ref
- Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. Text Summarization Branches Out (2004).Google Scholar
- Elena Lloret and Manuel Palomar. 2013. Towards automatic tweet generation: A comparative study from the text summarization perspective in the journalism genre. Expert Systems with Applications, Vol. 40, 16 (2013), 6624--6630.Google Scholar
Digital Library
- Miriam A Locher and Richard J Watts. 2008. Relational work and impoliteness: Negotiating norms of linguistic behaviour. Mouton de Gruyter.Google Scholar
- Marshall McLuhan. 1964. Understanding Media: The Extensions of Man .McGraw-Hill.Google Scholar
- J McPhee. 2015. Omission: Choosing what to leave out. https://web.archive.org/save/https://www.newyorker.com/magazine/2015/09/14/omission. The New Yorker (2015).Google Scholar
- Gaia Melloni, Ariela Caglio, and Paolo Perego. 2017. Saying more with less? Disclosure conciseness, completeness and balance in Integrated Reports. Journal of Accounting and Public Policy (2017).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 Scholar
Cross Ref
- Dhiraj Murthy. 2012. Towards a sociological understanding of social media: Theorizing Twitter. Sociology, Vol. 46, 6 (2012), 1059--1073.Google Scholar
- Seth A. Myers, Aneesh Sharma, Pankaj Gupta, and Jimmy Lin. 2014. Information network or social network? The structure of the Twitter follow graph. In Proc. International Conference on World Wide Web.Google Scholar
- Balder Onarheim and Michael Biskjaer. 2013. An introduction to creativity constraints. Proc. ISPIM Innovation Conference (2013).Google Scholar
- Balder Onarheim and Michael Biskjaer. 2015. Balancing constraints and the sweet spot as coming topics for creativity research. Creativity in Design: Understanding, Capturing, Supporting (2015).Google Scholar
- Daniel M Oppenheimer. 2006. Consequences of erudite vernacular utilized irrespective of necessity: Problems with using long words needlessly. Applied Cognitive Psychology, Vol. 20, 2 (2006), 139--156.Google Scholar
Cross Ref
- 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 Scholar
Cross Ref
- James W Pennebaker, Roger J Booth, and Martha E Francis. 2007. LIWC2007: Linguistic inquiry and word count. LIWC.net.Google Scholar
- Aaditya Prakash, Sadid A Hasan, Kathy Lee, Vivek Datla, Ashequl Qadir, Joey Liu, and Oladimeji Farri. 2016. Neural paraphrase generation with stacked residual LSTM networks. arXiv preprint arXiv:1610.03098 (2016).Google Scholar
- Eddie Shleyner. 2018. The ideal social media post length: A guide for every platform. https://web.archive.org/web/20181104085718/https://blog.hootsuite.com/ideal-social-media-post-length/.Google Scholar
- Zbynve k vS idák. 1967. Rectangular confidence regions for the means of multivariate normal distributions. J. Amer. Statist. Assoc., Vol. 62, 318 (1967), 626--633.Google Scholar
- Priya Sidhaye and Jackie Chi Kit Cheung. 2015. Indicative tweet generation: An extractive summarization problem?. In Proc. Conference on Empirical Methods in Natural Language Processing.Google Scholar
Cross Ref
- Brenda S Sloane. 2003. Say it straight: Teaching conciseness. Teaching English in the Two Year College (2003).Google Scholar
- 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.Google Scholar
Cross Ref
- Jacob Thebault-Spieker, Daniel Kluver, Maximilian A Klein, Aaron Halfaker, Brent Hecht, Loren Terveen, and Joseph A Konstan. 2017. Simulation experiments on (the absence of) ratings bias in reputation systems. In Proc. ACM Conference on Computer Supported Cooperative Work and Social Computing.Google Scholar
Digital Library
- 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
- Jui-Yu Weng, Cheng-Lun Yang, Bo-Nian Chen, Yen-Kai Wang, and Shou-De Lin. 2011. IMASS: An intelligent microblog analysis and summarization system. In Proc. Annual Meeting of the Association for Computational Linguistics.Google Scholar
- Richard Wright. 1997. Lexical competition and reduction in speech: A preliminary report. Research on Spoken Language Processing Progress Report, Vol. 2 (1997).Google Scholar
- Mark Yatskar, Bo Pang, Cristian Danescu-Niculescu-Mizil, and Lillian Lee. 2010. For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia. In Proc. Conference of the North American Chapter of the Association for Computational Linguistics.Google Scholar
- Wenpeng Yin and Hinrich Schütze. 2015. Convolutional neural network for paraphrase identification. In Proc. Conference of the North American Chapter of the Association for Computational Linguistics.Google Scholar
Cross Ref
Index Terms
Causal Effects of Brevity on Style and Success in Social Media
Recommendations
Sharing News Articles Using 140 Characters: A Diffusion Analysis on Twitter
ASONAM '12: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)Is it possible to effectively spread news articles to a large audience using 140 characters? How does the microblogging website Twitter get used as a platform for the news media agencies to create awareness about the articles they publish on a daily ...
Self-correcting mechanisms and echo-effects in social media
The positive and negative effects of social media in crises are currently receiving an increased amount of scholarly attention. This study focuses on Twitter users in the context of a crisis in the Netherlands on January 29, 2015. After having made a ...
Negative Messages Spread Rapidly and Widely on Social Media
COSN '15: Proceedings of the 2015 ACM on Conference on Online Social NetworksWe investigate the relation between the sentiment of a message on social media and its virality, defined as the volume and the speed of message diffusion. We analyze 4.1 million messages (tweets) obtained from Twitter. Although factors affecting message ...






Comments