Microsoft Technology Licensing, LLC

Paul Alexander Dow

Omar Rogelio Alonso

Junaid Ahmed

Peter Bailey

Scott Joseph Counts

Michael Ching

Nikhil Dandekar

Chad Carson

Lei Duan

Shubha Umesh Nabar

Walter Sun

Ho John Lee

Sasi Parthasarathy

Jingfeng Li

Hyun-Ju Seo

Shuang Guo

Yajie Siamwalla

Hemant Purohit

Aditya Pal

Architecture that automatically generate concise descriptions of users in social media. The descriptions communicate classification or category of a given social media user in a small amount of viewing space. The description can be based on available metadata (e.g., user profile biography) and/or other information about that person, as may be obtained from the information data sources (e.g., structured knowledge bases) on networks such as the Internet and enterprises, for example. The descriptions can also be query-dependent, by assuming there is some relationship between the social media user and a query, in which case, the descriptions illustrate that relationship.

Derivation and presentation of expertise summaries and interests for users

The author ranking technique described herein is a technique to rank authors in social media systems along various dimensions, using a variety of statistical methods for utilizing those dimensions. More particularly, the technique ranks authors in social media systems through a combination of statistical techniques that leverage usage metrics, and social and topical graph characteristics. In various exemplary embodiments, the technique can rank author authority by the following: 1) temporal analysis of link sharing in which authority is computed based on a user's propensity to provide early links to web pages that subsequently become popular; 2) topical authority based on the author's links and content updates in specific topic areas; and 3) popularity and influence based on nodal properties of authors.

Ranking authors in social media systems

Methods, systems, and computer-readable media are provided for updating a search index with new uniform resource locators (URLs) and with metadata for new and known URLs. Data associated with communications made by users using a social network is received. The communications include a URL therein that a user has shared, posted, or otherwise communicated to one or more other users using the social network. When the URL is not found in a search index it is identified as a new URL and is added to the search index. A measure of a trending interest, or virality, of the URL is determined from the data. The determined virality is associated with the URL in a search index as metadata. The virality is useable to inform a ranking of the URL against a plurality of other URLs for identification and presentation as a search result in a search engine results page.

Using social-network data for identification and ranking of URLs

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Paul Alexander Dow