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Note: This post is co-written with Piotr Sapieżyński Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter, a massive social network with hun...
A large part of our motivation for investigating Twitter bots in class is that the amount of manipulation that humans are experiencing on line is ever increasing. Think, for example, about how Facebook’s time-line filtering algorithm shapes the world view of hundreds of millions around the globe. And that’s just the most main stream example.
As the course progressed, we focused on creating bots that could use machine learning to recognize “good” content for tweeting and retweeting. Bots that are able to detect topics within their tweet-stream … and distinguish between real, human accounts and robots among their followers.
However, the question remained: Can those thousands of followers be converted to influence on Twitter? For the class’ final project, we decided to put that to the test.
The overall goal was to for each team to build a convincing bot, get human followers, and at a specified time, for everyone work together to make specific hashtags trend on twitter. So how to achieve that goal? Here’s an overview of what each team has worked on:
- Build convincing avatars and use the high follower-counts as part of the disguise.
- Use machine learning to tell who’s a bot and who’s not (in order to focus only on humans and ignoring bots).
- Use natural language processing & machine learning to discover quality content to re-tweet and tweet.
- Use network theory, to explore the network surrounding existing followers, making sure that bot actions reach entire communities.
We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
Another paper about popularity prediction.