Title: Early Detection of Promoted Campaigns on Social Media,
Abstract: Social media expose millions of users every day to information campaigns ---
some emerging organically from grassroots activity, others sustained by
advertising or other coordinated efforts. These campaigns contribute to the
shaping of collective opinions. While most information campaigns are benign,
some may be deployed for nefarious purposes. It is therefore important to be
able to detect whether a meme is being artificially promoted at the very moment
it becomes wildly popular. This problem has important social implications and
poses numerous technical challenges. As a first step, here we focus on
discriminating between trending memes that are either organic or promoted by
means of advertisement. The classification is not trivial: ads cause bursts of
attention that can be easily mistaken for those of organic trends. We designed
a machine learning framework to classify memes that have been labeled as
trending on Twitter.After trending, we can rely on a large volume of activity
data. Early detection, occurring immediately at trending time, is a more
challenging problem due to the minimal volume of activity data that is
available prior to trending.Our supervised learning framework exploits hundreds
of time-varying features to capture changing network and diffusion patterns,
content and sentiment information, timing signals, and user meta-data. We
explore different methods for encoding feature time series. Using millions of
tweets containing trending hashtags, we achieve 75% AUC score for early
detection, increasing to above 95% after trending. We evaluate the robustness
of the algorithms by introducing random temporal shifts on the trend time
series. Feature selection analysis reveals that content cues provide
consistently useful signals; user features are more informative for early
detection, while network and timing features are more helpful once more data is
available.