Abstract: A system for processing social media data includes a platform with a social media acquisition module configurable to collect a plurality of social media statements. The platform further includes an analysis engine configurable to analyze the plurality of social media statements according to a first sentiment model to generate first analytics data. The analysis engine is configurable to present the first analytics data to a client user, including a display of a sentiment value for each of the social media statements. The platform further includes a feedback queue configurable to receive feedback from the client user on at least a portion of the sentiment values; a model modification module configurable to modify the first sentiment model based on the feedback to result in a modified sentiment model; and a database configurable to store the modified sentiment model as a personalized sentiment model for the client user.

Abstract: A computer-implemented method is presented herein. The method obtains a first content item from an online source, and then generates a characterizing signature of the first content item. The method continues by finding a previously-saved instance of the characterizing signature and retrieving data associated with a second content item (the second content item is characterized by the characterizing signature). The method continues by analyzing the data associated with the second content item, corresponding data associated with the first content item, and decision criteria. Thereafter, either the first content item or the second content item is identified as an original content item, based on the analyzing. The other content item can be flagged as an aggregated content item.

Abstract: A computer-implemented method and system are provided in which characteristics of a website are analyzed to determine whether the website represents a potential source of spam content. The analysis can include generating a characterizing signature of a webpage containing a content item, and obtaining an occurrence count for the generated characterizing signature. The characterizing signature is derived from formatting data of the webpage. When the obtained occurrence count is greater than a threshold count, the content item can be identified as spam content, and flagged as spam content.

Abstract: A computer-implemented method and system are provided in which characteristics of a website are analyzed to determine whether the website represents a potential source of spam, and, in response to the analyzing, flags content of the website as spam content. To analyze the website, a total number of posts associated with the website is computed and a publication frequency for the total number of posts can then be calculated. Based on the computed total number and the calculated publication frequency, it can be determined whether the website in its entirety represents spam content. For instance, the calculated publication frequency can be compared to a threshold frequency, and when the calculated publication frequency is greater than the threshold frequency, the website in its entirety as can be identified and flagged as spam content.

Abstract: A computer-implemented method analyzes a website to determine whether the website represents a potential source of spam, and, in response to the analyzing, flags content of the website as spam content. The determination can be made by computing a total number of content items associated with the website, calculating a publication frequency of the content items, and determining whether the website in its entirety represents spam content based on the total number and the publication frequency. The determination could also be made by generating a signature of a webpage containing a content item, obtaining an occurrence count for the generated characterizing signature, and, when the obtained occurrence count is greater than a threshold count, identifying the content item as spam.

Abstract: A computer-implemented method is presented herein. The method obtains a first content item from an online source, and then generates a characterizing signature of the first content item. The method continues by finding a previously-saved instance of the characterizing signature and retrieving data associated with a second content item (the second content item is characterized by the characterizing signature). The method continues by analyzing the data associated with the second content item, corresponding data associated with the first content item, and decision criteria. Thereafter, either the first content item or the second content item is identified as an original content item, based on the analyzing. The other content item can be flagged as an aggregated content item.

Abstract: A computer-implemented method is presented herein. The method obtains a first content item from an online source, and then generates a characterizing signature of the first content item. The method continues by finding a previously-saved instance of the characterizing signature and retrieving data associated with a second content item (the second content item is characterized by the characterizing signature). The method continues by analyzing the data associated with the second content item, corresponding data associated with the first content item, and decision criteria. Thereafter, either the first content item or the second content item is identified as an original content item, based on the analyzing. The other content item can be flagged as an aggregated content item.

Abstract: A computer-implemented method is presented herein. The method obtains a first content item from an online source, and then generates a characterizing signature of the first content item. The method continues by finding a previously-saved instance of the characterizing signature and retrieving data associated with a second content item (the second content item is characterized by the characterizing signature). The method continues by analyzing the data associated with the second content item, corresponding data associated with the first content item, and decision criteria. Thereafter, either the first content item or the second content item is identified as an original content item, based on the analyzing. The other content item can be flagged as an aggregated content item.

Abstract: A computer-implemented method analyzes a website to determine whether the website represents a potential source of spam, and, in response to the analyzing, flags content of the website as spam content. The determination can be made by computing a total number of content items associated with the website, calculating a publication frequency of the content items, and determining whether the website in its entirety represents spam content based on the total number and the publication frequency. The determination could also be made by generating a signature of a webpage containing a content item, obtaining an occurrence count for the generated characterizing signature, and, when the obtained occurrence count is greater than a threshold count, identifying the content item as spam.

Abstract: A computer-implemented method is disclosed herein. The method obtains a first content item from an online source, and selects only a portion of the words in the first content item, in accordance with a defined selection algorithm. The method continues by generating a characterizing signature of the first content item, wherein the characterizing signature is derived from the selected words. The method continues by finding a previously-saved instance of the characterizing signature, retrieving data associated with a second content item (which is linked to the saved characterizing signature), and comparing the data associated with the second content item against corresponding data associated with the first content item. The first content item or the second content item is identified as an original content item, based on the comparing. The other content item is flagged as an aggregated content item.