The Tweets They Are a-Changin: Evolution of Twitter Users and Behavior

By Yabing Liu, Chloe Kliman-Silver, Alan Mislove. 2014

160 citations recorded by the source corpus.

The microblogging site Twitter is now one of the most popular Web destinations. Due to the relative ease of data access, there has been significant research based on Twitter data, ranging from measuring the spread of ideas through society to predicting the behavior of real-world phenomena such as the stock market. Unfortunately, relatively little work has studied the changes in the Twitter ecosystem itself; most research that uses Twitter data is typically based on a small time-window of data, generally ranging from a few weeks to a few months. Twitter is known to have evolved significantly since its founding, and it remains unclear whether prior results still hold, and whether the (often implicit) assumptions of proposed systems are still valid. In this paper, we take a first step towards answering these question by focusing on the evolution of Twitter's users and their behavior. Using a set of over 37 billion tweets spanning over seven years, we quantify how the users, their behavior, and the site as a whole have evolved. We observe and quantify a number of trends including the spread of Twitter across the globe, the rise of spam and malicious behavior, the rapid adoption of tweeting conventions, and the shift from desktop to mobile usage. Our results can be used to interpret and calibrate previous Twitter work, as well as to make future projections of the site as a whole.

Subfields: Information Systems, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Further, in the case of Twitter, much research relies on APIs that give access to at most 1% of the data (González-Bailón et al., 2014a; Joseph et al., 2014; Morstatter et al., 2014).”
Population Bias in Geotagged Tweets (2015, methodology): “Also worth noting is that Twitter has undergone large changes since the data used by Mislove et al., both in the governance and management of the platform itself (van Dijck 2013) and in patterns of user behavior (Liu, Kliman-Silver, and Mislove 2014).”
Everyone's Invited: A New Paradigm for Evaluation on Non-Transferable Datasets (2015, background): “…refine large datasets to only the pertinent data; for example, experiments with Twitter commonly filter data to only those a connection to databases that supports specific queries. posts written in English, which can remove over half of all posts (Liu, Kliman-Silver, and Mislove 2014).”
Measuring Social Jetlag in Twitter Data (2016, background): “Liu, Kliman-Silver, and Mislove (2014) present a comprehensive overview of tweets sent 2006-2014 including user characteristics, languages, etc. Rios and Lin (2013) analyse the temporal footprint of all geo-tagged tweets from several major cities and observe later peaks on the weekends as well as…”
Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter (2015, background): “Given the increasing attrition rate of Twitter users over time (Liu, Kliman-Silver, and Mislove 2014), we speculate that disinterested individuals slow their activity as their account ages before finally leaving the site, leading to a lower average tweet rate; in contrast, organizational accounts…”