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      Diffusion of Lexical Change in Social Media

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          Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity – especially with regard to race – plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified “netspeak” dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.

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          Most cited references 15

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          Social science. Computational social science.

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            A 61-million-person experiment in social influence and political mobilization.

            Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between 'close friends' who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.
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              Identifying influential and susceptible members of social networks.

               S Aral,  Dylan Walker (2012)
              Identifying social influence in networks is critical to understanding how behaviors spread. We present a method that uses in vivo randomized experimentation to identify influence and susceptibility in networks while avoiding the biases inherent in traditional estimates of social contagion. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible to influence than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product offered. Analysis of influence and susceptibility together with network structure revealed that influential individuals are less susceptible to influence than noninfluential individuals and that they cluster in the network while susceptible individuals do not, which suggests that influential people with influential friends may be instrumental in the spread of this product in the network.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                19 November 2014
                : 9
                : 11
                [1 ]School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, United States of America
                [2 ]School of Computer Science, University of Massachusetts, Amherst, Massachusetts, United States of America
                [3 ]School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
                Massachusetts Institute of Technology, United States of America
                Author notes

                Competing Interests: BO and NAS were supported by Google's support of the Reading is Believing project at Carnegie Mellon University. This study was also supported by a computing resources award from Amazon Web Services. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: JE BO NAS EPX. Performed the experiments: JE BO. Analyzed the data: JE BO. Wrote the paper: JE BO NAS.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Pages: 13
                This work was supported by National Science Foundation grants IIS-1111142 and IIS-1054319, by Google's support of the Reading is Believing project at Carnegie Mellon University, a computing resources award from Amazon Web Services. This work was supported by computing resources from the Open Source Data Cloud (OSDC), which is an Open Cloud Consortium (OCC)-sponsored project. OSDC usage was supported in part by grants from Gordon and Betty Moore Foundation and the National Science Foundation, and by major contributions from OCC members like the University of Chicago. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Biology and Life Sciences
                Cognitive Science
                Artificial Intelligence
                Machine Learning
                Computer and Information Sciences
                Physical sciences
                Statistics (mathematics)
                Statistical methods
                Monte Carlo method
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Social Sciences
                Computational Linguistics
                Linguistic Geography
                Custom metadata
                The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. The text data in this paper was acquired from Twitter's streaming API, and redistribution of the raw text is prohibited by their terms of service (TOS). A complete word list and the associated annotations are provided as a supporting document. Public dissemination of the Tweet IDs will enable other researchers to obtain this data from Twitter's API, except for messages which have been deleted by their authors. Tweet IDs can be obtained by emailing the corresponding author.



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