Abstract: Aspects of the disclosure provide for detection of spam business listings. Aspects operate to identify business listing characteristics in trusted sources and untrusted sources. As untrusted sources are likely to contain more spam, characteristics that are present in untrusted sources but not present in trusted sources are typically indicative of spam listings, and vice versa. Thus, statistical analysis of the frequency of characteristics within each source may be used to identify common characteristics of spam listings. These characteristics may further be analyzed in specific listing contexts, as different listing contexts (e.g., different types of businesses) typically use different terms and vocabularies, such that terms that are indicative of spam in one context may not be indicative of spam in another. Various methods for leveraging this context-specific statistical information to improve spam detection operations are disclosed.

Abstract: Aspects of the disclosure provide for detection of spam business listings. Aspects operate to identify business listing characteristics in trusted sources and untrusted sources. As untrusted sources are likely to contain more spam, characteristics that are present in untrusted sources but not present in trusted sources are typically indicative of spam listings, and vice versa. Thus, statistical analysis of the frequency of characteristics within each source may be used to identify common characteristics of spam listings. These characteristics may further be analyzed in specific listing contexts, as different listing contexts (e.g., different types of businesses) typically use different terms and vocabularies, such that terms that are indicative of spam in one context may not be indicative of spam in another. Various methods for leveraging this context-specific statistical information to improve spam detection operations are disclosed.

Abstract: Methods, computer-readable media, and systems for determining spam in information collected by a source are described. A frequency of occurrence of a phrase included in text items received from a source is determined. The text items are associated with business entities and do not include any spam. Another frequency of occurrence of a phrase included in text items received from another source is determined. The text items received from the other source may or may not include spam. From the frequencies, likelihoods that a phrase is spam are determined. From the likelihoods, another likelihood that a different text item includes spam is determined.

Abstract: A computer-implemented method includes identifying a plurality of ratings on a plurality of items, wherein the plurality of ratings are made by a first user, determining one or more differences between the plurality of ratings, and ratings by other users associated with the items, and generating a quality score for the first user using the one or more differences.