YouTube videos are a popular medium for online product reviews. They are not only informative and entertaining, but may also be perceived as quite credible under the viewer’s impression of a personal product demonstration by an expert. As the world’s largest online video platform, YouTube’s content is included prominently in the results of most general-purpose web search engines. Consequently, online marketeers are using classic Search Engine Optimization (SEO) techniques also for placing their video content in search engines. Over the years, we have noticed an ever increasing noise floor of low-quality SEO content in product search results and in this study, we show that this trend has spilled over into videos as well. We examine YouTube video reviews for several thousand products retrieved from three commercial search engines and conduct spam detection experiments based directly on the videos’ subtitle transcripts rather than relying on metadata and comments. We find that at least a third of the retrieved videos can be regarded as spam or low-quality productions. We are further able to distinguish these spam product reviews accurately from higher-quality videos with a semi-supervised n-gram classification approach.

Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84.