Salesforce Com, Inc.

Q1 Labs Inc.

International Business Machines Corporation

Christopher Daniel Newton

Marcel Albert Lebrun

Christopher Bennett Ramsey

Christopher Wayne Fanjoy

Jason Leger

William Alexander Bird

Derek Brian Hatchard

Timothy Frederic Beatty

Gregory Marc Picot

A method and system for determining on-line influence in social media is disclosed. A recursive site influence modeling module computes a site influence from aggregated viral properties of content hosted by the site and further integrates, in the formulation of the site influence model, the influence of commentors, commenting on the hosted content, and the influence of individuals cited in the content. The influence of the commentors and individuals is calculated by aggregating viral properties of related content and as well by taking into account the influence of outlets owned by the commentors and the individuals.

Method and system for determining on-line influence in social media

Methods and systems for compiling marketing information for a client are provided. Data can be obtained from a plurality of social media websites, where each of the social media websites includes a universal resource identifier that points to a client website. A plurality of insights can be extracted from the obtained data, resulting in extracted insights. Each of the extracted insights comprises text elements that denote product approval for at least one product available for sale at the client website. Measurements of traffic to the client website can be collected, the traffic being referred to the client website by the plurality of social media websites. A referred traffic dynamics summary table can then be generated based on the extracted insights and the measurements of traffic. The referred traffic dynamics summary table aggregates the extracted insights across the plurality of social media websites to rank the extracted insights.

Methods and systems for compiling marketing information for a client

Methods and systems are provided for processing social media data. The method includes receiving a request from a user for social media analytics of social media data; sending the social media data to a first analytics provider and a second analytics provider; receiving first analytics data from the first data analytics provider and second analytics data from the second analytics provider; storing the first analytics data and the second analytics data in a database; and presenting the first analytics data and the second analytics data to the user.

Methods and systems for processing social media data

Systems and methods are provided for collecting and analyzing packet data flows. A flow collector generates flow data records based on each packet that is observed at one or more network monitoring points. A flow processor receives the flow data records from one or more flow collectors and examines the flow data records to identify related flows. Further features, such as content capture and application identification, are also provided.

Network data flow collection and processing

Methods and a system for exploiting the secondary traffic generated by social networking sites are disclosed. Traffic on a commercial website is constantly monitored by a web analytics tool, which collects traffic measurements of hits, button presses, enquiries, purchases etc., as well as the referrer URL of a site such as a social networking site through which the commercial website is accessed. The collected measurements are forwarded to a Referred Traffic Analysis System. Concurrently, the Referred Traffic Analysis System crawls the Internet and collects a large number of social networking sites, analyses their content by extracting insight terms and phrases from them. In a third step, the collected traffic measurements are correlated with the collected insights from the social networking sites, and the top insights that reoccur frequently enough to appear to be a driver for the measurements observed, are presented to the client. A corresponding system is also provided.

Referred internet traffic analysis system and method

A targeted advertising system and method based on memes contained in content sources are disclosed. Content matching keywords-defining topics are identified from content sources and are further processed to extract the memes. Ad networks servicing the content are also identified and their reach for each meme determined. The system and method extract also viral dynamics of the content associated to a meme and use the aggregation of the viral dynamics as a measure of engagement level for the meme. The system and method allow a Marketer to select a meme based on the engagement level and to run an ad campaign against the meme. The advertisements are delivered through an Ad network and inserted at the meme page level when the content hosting the meme is accessed, the Ad network being selected based on its reach.

Method and system for targeted advertising based on topical memes

Methods and a system for exploiting the secondary traffic generated by social networking sites are disclosed. Traffic on a commercial website is constantly monitored by a web analytics tool, which collects traffic measurements of hits, button presses, enquiries, purchases etc, as well as the referrer URL of a site such as a social networking site through which the commercial website is accessed. The collected measurements are forwarded to a Referred Traffic Analysis System. Concurrently, the Referred Traffic Analysis System crawls the Internet and collects a large number of social networking sites, analyses their content by extracting insight terms and phrases from them. In a third step, the collected traffic measurements are correlated with the collected insights from the social networking sites, and the top insights that reoccur frequently enough to appear to be a driver for the measurements observed, are presented to the client. A corresponding system is also provided.

A security incident manger includes events and network flows in the analysis of an attack to better identify the magnitude of the attack and how to handle the situation. The raw events are reported by monitored devices and the incident manager may request network flows from various devices corresponding to a raw event. The manager then assigns a variable score to the severity, the relevance and the credibility of the event to determine its next processing steps. Those events that appear to be a likely and effective attack are classified as offenses. Offenses are stored in order to provide additional data for evaluating future events and for building a 'rap sheet' against repeat attackers and repeat events.

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Douglas, CA, United States of America

Christopher Daniel Newton