Abstract: A new approach is proposed that contemplates systems and methods to determine temporality of a query in order to generate a search result including a list of objects that are not only based on matching of the objects to the query but also based on temporality analysis of the query. Here, the temporality of the query can be defined as the distribution over time of the objects matching the query, i.e., the chronology histogram of the query. Such distribution can be analyzed to provide a classification of the intent of the query. Classification of the intent of the query can result either in discrete classification of the query into categories, or in continuous classification of the query which may be a scalar or vector value resulting from transformations of the chronology histogram.

Abstract: Advertising based on influence is provided. In some embodiments, advertising based on influence includes determining an influence score (e.g., based on a given dimension) for a subject (e.g., a user), in which the subject is a potential target for an advertisement; and determining targeting of the advertisement based on criteria including the influence score of potential recipients of the advertisement. In some embodiments, the influence score is a directly estimated objective measure of influence (e.g., estimated using a social graph). In some embodiments, advertising based on influence also includes determining pricing of advertisements based on criteria including the influence score of potential recipients of one or more advertisements. In some embodiments, advertising based on influence further includes sharing advertising revenue with the subject based on criteria including the influence score of the first subject (e.g., as an incentive for the subject to view the advertisement).

Abstract: Advertising based on influence is provided. In some embodiments, advertising based on influence includes determining an influence score (e.g., based on a given dimension) for a subject (e.g., a user), in which the subject is a potential target for an advertisement; and determining targeting of the advertisement based on criteria including the influence score of potential recipients of the advertisement. In some embodiments, the influence score is a directly estimated objective measure of influence (e.g., estimated using a social graph). In some embodiments, advertising based on influence also includes determining pricing of advertisements based on criteria including the influence score of potential recipients of one or more advertisements. In some embodiments, advertising based on influence further includes sharing advertising revenue with the subject based on criteria including the influence score of the first subject (e.g., as an incentive for the subject to view the advertisement).

Abstract: Ranking and selecting entities based on calculated reputation or influence scores is provided. In some embodiments, a method includes determining whether a first entity is a subject or an object; determining whether a second entity is a subject or an object; and generating a graph, in which a subset of the graph is a subject graph of subject nodes that includes at least one or more subjects (e.g., subject entities) linked to one or more other subjects, and in which the graph includes one or more objects (e.g., object entities) each linked to one or more subjects in the subject graph. In some embodiments, the graph includes directed and undirected links. In some embodiments, the graph includes one or more objects linked to one or more objects.

Abstract: A new approach is proposed that contemplates systems and methods to identify geographic locations of all social media content items retrieved form a social network in real time, wherein the geographic locations are physical locations from which the social media content items are originated or authored. If the latitude/longitude (geographic) coordinates of the content item are available, the geographic location of the social media content item can be identified using such geographic coordinates. For content items which geographic coordinates are not available, historical archive of content items with high-confidence of geographic locations are utilized to train a location classifier to predict geographic locations of such content items with high accuracy. Finally, the identified locations of the content items are confirmed to be accurate and are presented to a user together with the content items.

Abstract: A new approach is proposed that contemplates systems and methods to discover one or more terms related to one or more query terms submitted by a user for search over a social media network, wherein the related terms discovered are trending and co-occurring with the submitted query terms over the social media network during a specific period of time. The terms related to the submitted keywords can be discovered based on based on various measurements that measure the trending characteristics of the terms in the social media content items collected over a period of time.

Abstract: A new approach is proposed that contemplates systems and methods to filter and/or rank a plurality of content items retrieved from a social network based on the sentiments expressed by the authors of the content items and/or the influence level of the authors over the social network. First, content items matching a set of keywords submitted by a user are retrieved from the social network. The sentiments and/or the influence levels of the authors of these content items are then identified in real time. Once identified, the sentiments and/or influence levels of the authors are used to filter and/or rank the retrieved content items to generate a search result that matches with the sentiment and/or influence level specified by the user. Finally, the customized search result based on the sentiments and/or the influence levels of the authors is presented to the user.

Abstract: A new approach is proposed that contemplates systems and methods to generate customized search results as well as metrics, such as aggregated sentiment, counts of targets or sources or citations, or aggregated gross impressions or exposure, of social media content items over a social network while discriminating between the perspectives of individuals from the media and individuals not from the media. This approach can be used to generate search results and/or metrics including only media perspectives, or excluding media perspectives. More specifically, while social media content items are retrieved from corpus based on certain search criteria, for the purpose of providing search results or providing aggregated metrics, the search criteria can include a media or non-media filter, which is applied to the authors posting social media content to exclude or include certain authors meeting media/non-media criteria.

Abstract: A new approach is proposed that contemplates systems and methods to provide the ability to detect, measure, aggregate, and normalize sentiments expressed by a group of users on a certain event or topic on a social network so that the normalized sentiments truly reflect the sentiments of the general public on that specific event or topic. Additionally, the collected and measured sentiments of an individual user expressed on a social network can also be normalized against a baseline sentiment that reflects in order to truly reflect the individual user's sentiment at the time of his/her expression.

Abstract: A new approach is proposed that contemplates systems and methods to support efficient crawling of a social media network based on predicted future activities of each user on the social network. First, data related to a user's past activities on a social network are collected and a pattern of the user's past activities over time on the social network is established. Based on the established pattern on the user's past activities, predictions about the user's future activities on the social network can be established. Such predictions can then be used to determine the collection schedule—timing and frequency—to collect data on the user's activities for future crawling of the social network.

Abstract: Ranking and selecting entities based on calculated reputation or influence scores is provided. In some embodiments, a method includes determining whether a first entity is a subject or an object; determining whether a second entity is a subject or an object; and generating a graph, in which a subset of the graph is a subject graph of subject nodes that includes at least one or more subjects (e.g., subject entities) linked to one or more other subjects, and in which the graph includes one or more objects (e.g., object entities) each linked to one or more subjects in the subject graph. In some embodiments, the graph includes directed and undirected links. In some embodiments, the graph includes one or more objects linked to one or more objects.

Abstract: Estimating influence includes receiving a subject graph, in which the subject graph includes two or more subject nodes, in which each subject node corresponds to a subject; and determining an objective influence measure for each first subject node of the subject graph, in which the determination is based at least on part on a function of inward scores and outward scores, in which inward scores are computed from one or more paths leading to the first subject of a length of at least one, and outward scores are computed from one or more paths leading from the first subject of a length of at least one.

Abstract: Systems and methods allowing for the attribution of reputation to data sources (e.g., for the creation of referrals) are provided. In an illustrative implementation scores (e.g., reputation scores) are determined for a target entity connected a source entity on a network on a given dimension. In the illustrative implementation, an entity may be directly linked to any number of other entities on any number of dimensions, with each link having an associated score. Illustratively, each dimension has an associated transitive dimension. A directed path on a given dimension between two entities, a source and a target, consists of a directed link from the source entity to an intermediate entity, prefixed to a directed path from the intermediate entity to the target entity. In the illustrative implementation, links on the path can travel on the transitive dimension associated with the given dimension.

Abstract: Systems and methods allowing for the attribution of reputation to data sources (e.g., for the creation of referrals) are provided. In an illustrative implementation scores (e.g., reputation scores) are determined for a target entity connected a source entity on a network on a given dimension. In the illustrative implementation, an entity may be directly linked to any number of other entities on any number of dimensions, with each link having an associated score. Illustratively, each dimension has an associated transitive dimension. A directed path on a given dimension between two entities, a source and a target, consists of a directed link from the source entity to an intermediate entity, prefixed to a directed path from the intermediate entity to the target entity. In the illustrative implementation, links on the path can travel on the transitive dimension associated with the given dimension.

Abstract: Systems and methods allowing for the attribution of reputation to data sources (e.g., for the creation of referrals) are provided. In an illustrative implementation scores (e.g., reputation scores) are determined for a target entity connected a source entity on a network on a given dimension. In the illustrative implementation, an entity may be directly linked to any number of other entities on any number of dimensions, with each link having an associated score. Illustratively, each dimension has an associated transitive dimension. A directed path on a given dimension between two entities, a source and a target, consists of a directed link from the source entity to an intermediate entity, prefixed to a directed path from the intermediate entity to the target entity. In the illustrative implementation, links on the path can travel on the transitive dimension associated with the given dimension.

Abstract: Estimating influence is provided. For example, estimating influence includes determining an objective influence measure, which can be applied to various applications (e.g., search using objective influence).

Abstract: Mediating and pricing transactions based on calculated reputation and influence is provided. In some embodiments, mediating and pricing transactions based on calculated reputation and influence includes determining an influence score (e.g., based on a given dimension) for a subject (e.g., a user), in which the subject is requesting a transaction; and determining approval of the transaction based on criteria including the influence score of the subject. In some embodiments, the influence score is a directly estimated objective measure of influence (e.g., estimated using a social graph). In some embodiments, mediating and pricing transactions based on calculated reputation and influence further includes determining pricing of the transaction based on criteria including the influence score of the subject.

Abstract: Ranking and selecting entities based on calculated reputation or influence scores is provided. In some embodiments, a method includes determining whether a first entity is a subject or an object; determining whether a second entity is a subject or an object; and generating a graph, in which a subset of the graph is a subject graph of subject nodes that includes at least one or more subjects (e.g., subject entities) linked to one or more other subjects, and in which the graph includes one or more objects (e.g., object entities) each linked to one or more subjects in the subject graph. In some embodiments, the graph includes directed and undirected links. In some embodiments, the graph includes one or more objects linked to one or more objects.

Abstract: Advertising based on influence is provided. In some embodiments, advertising based on influence includes determining an influence score (e.g., based on a given dimension) for a subject (e.g., a user), in which the subject is a potential target for an advertisement; and determining targeting of the advertisement based on criteria including the influence score of potential recipients of the advertisement. In some embodiments, the influence score is a directly estimated objective measure of influence (e.g., estimated using a social graph). In some embodiments, advertising based on influence also includes determining pricing of advertisements based on criteria including the influence score of potential recipients of one or more advertisements. In some embodiments, advertising based on influence further includes sharing advertising revenue with the subject based on criteria including the influence score of the first subject (e.g., as an incentive for the subject to view the advertisement).