Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums
This addresses the challenge of timely and effective comment filtering for users and researchers analyzing social media news forums, though it appears incremental in its approach.
The paper tackles the problem of extracting valuable insights from large-scale social media comments by designing efficient, single-pass adaptive natural language filters to prune spam and noise, removing over a third of entries and increasing the probability of relatedness to the original article.
There are large amounts of insight and social discovery potential in mining crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com, Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of participation with its varying quality of commentary, extracting value out of such data isn't always obvious nor timely. By designing efficient, single-pass and adaptive natural language filters to quickly prune spam, noise, copy-cats, marketing diversions, and out-of-context posts, we can remove over a third of entries and return the comments with a higher probability of relatedness to the original article in question. The approach presented here uses an adaptive, two-step filtering process. It first leverages the original article posted in the thread as a starting corpus to parse comments by matching intersecting words and term-ratio balance per sentence then grows the corpus by adding new words harvested from high-matching comments to increase filtering accuracy over time.