2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
Fred Morstatter , Arizona State University, Tempe, Arizona, 85281
Liang Wu , Arizona State University, Tempe, Arizona, 85281
Tahora H. Nazer , Arizona State University, Tempe, Arizona, 85281
Kathleen M. Carley , Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213
Huan Liu , Arizona State University, Tempe, Arizona, 85281
The presence of bots has been felt in many aspects of social media. Twitter, one example of social media, has especially felt the impact, with bots accounting for a large portion of its users. These bots have been used for malicious tasks such as spreading false information about political candidates and inflating the perceived popularity of celebrities. Furthermore, these bots can change the results of common analyses performed on social media. It is important that researchers and practitioners have tools in their arsenal to remove them. Approaches exist to remove bots, however they focus on precision to evaluate their model at the cost of recall. This means that while these approaches are almost always correct in the bots they delete, they ultimately delete very few, thus many bots remain. We propose a model which increases the recall in detecting bots, allowing a researcher to delete more bots. We evaluate our model on two real-world social media datasets and show that our detection algorithm removes more bots from a dataset than current approaches.
Twitter, Labeling, Feature extraction, Suspensions, Tagging, Pattern matching
F. Morstatter, L. Wu, T. H. Nazer, K. M. Carley and H. Liu, "A new approach to bot detection: Striking the balance between precision and recall," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 533-540.