System and method for determining and delivering breaking news utilizing social media
Disclosed are systems and methods for improving interactions with and between computers in a content system supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data across platforms, which data can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods determine breaking or trending news stories from social media activity, which are then communicated to users via personalized delivery. Thus, the disclosed systems and methods leverage social data, expert knowledge and/or user feedback, which are all available on-line, to determine breaking news stories, which are then delivered to users in a personalized manner specific to each user.
1. A method comprising:
determining, via a computing device, a trusted news source from a plurality of news sources, said trust determination based on a determination that the trusted news source communicates newsworthy content via a social media platform, said trusted news source having an associated framing set comprising information associating the trusted news source with a content type;
determining, via the computing device, a plurality of trusted secondary news sources based on the framing set of the trusted news source, said secondary trust determination comprising parsing messages sent by the secondary news sources and identifying content in said messages that matches the framing set information at or above a threshold;
analyzing, via the computing device over a network, a plurality of new messages communicated by the trusted secondary news sources, and based on said analysis, detecting a topic commonly mentioned in at least a predetermined number of the new messages;
identifying, via the computing device over the network, a social media message respectively communicated by a plurality of users;
filtering, via the computing device, the social media messages communicated by the plurality of users;
identifying, based on said filtering of the social media messages communicated by the plurality of users, via the computing device, a message subset, each message in the message subset comprising content associated with a common event, each message in the message subset being associated with the detected topic and communicated during a predetermined time window that commences upon the detection of the topic commonly mentioned in at least the predetermined number of new messages;
analyzing, via the computing device, user data of users on a social media platform, said user data providing indications of online and real-world activity performed by the user, said analysis comprising parsing said user data based on said content of the message subset;
determining, via the computing device, based on said analysis, a set of users to be interested in said detected topic;
generating, via the computing device, a new message serving as a breaking news content message, said new message comprising at least said content of the message subset; and
communicating, via the computing device over a network, the new message to said set of interested users.
2. The method of claim 1 , wherein said topic detection comprises:
extracting content from each of said plurality of new messages, said extracting comprising determining a contiguous sequence of items from said content;
performing natural language processing (NLP) on the extracted content, said NLP comprises identifying stop words, where the items associated with said stop words are removed from said sequence; and
determining relationships between items associated with remaining content in each sequence that satisfy a topic threshold.
3. The method of claim 2 , further comprising:
clustering each item satisfying the topic threshold based on said relationships, said clustered items being associated with a common category, wherein said detected topic is associated with said common category.
4. The method of claim 1 , further comprising:
identifying a plurality of messages sent by said trusted news source via the social media platform;
parsing each of said plurality of messages from the trusted news source to identify characteristics of each message, said characteristics being said information associating the trusted news source with the content type, wherein said framing set is based on said characteristics of each message communicated by the trusted news source; and
storing said framing set in a database in association with said trusted news source.
5. The method of claim 4 , wherein said identifying and parsing steps are performed for each identified trusted news source across a plurality of social media platforms.
6. The method of claim 1 , wherein said trusted news source is determined to be newsworthy based on information selected from a group consisting of: the trusted news source having a number of followers or friends on the social media platform satisfying a threshold, the trusted news source's messages have been shared, quoted or forwarded a predetermined number of times during a threshold time period, the trusted news source is a verified user of a the social media platform, and the trusted news source is associated with an established media outlet.
7. The method of claim 1 , wherein said new message is a push message, and said communication comprises facilitating sending the new message via at least one social media platform associated with each of the set of users.
8. The method of claim 1 , wherein said communication of the new message comprises a message selected from a group consisting of: forwarding a message comprising content associated with said detected topic, quoting a message comprising content associated with said detected topic, generating a new message comprising content associated with said detected topic, and referencing a message comprising content associated with said detected topic.
9. The method of claim 1 , wherein said plurality of new messages communicated by the trusted secondary news sources comprises social media activity occurring across a plurality of social media platforms.
10. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a computing device, performs a method comprising:
determining a trusted news source from a plurality of news sources, said trust determination based on a determination that the trusted news source communicates newsworthy content via a social media platform, said trusted news source having an associated framing set comprising information associating the trusted news source with a content type;
determining a plurality of trusted secondary news sources based on the framing set of the trusted news source, said secondary trust determination comprising parsing messages sent by the secondary news sources and identifying content in said messages that matches the framing set information at or above a threshold;
analyzing, over a network, a plurality of new messages communicated by the trusted secondary news sources, and based on said analysis, detecting a topic commonly mentioned in at least a predetermined number of the new messages;
identifying, over the network, a social media message respectively communicated by a plurality of users;
filtering the social media messages communicated by the plurality of users;
identifying, based on said filtering of the social media messages communicated by the plurality of users, a message subset, each message in the message subset comprising content associated with a common event, each message in the message subset being associated with the detected topic and communicated during a predetermined time window that commences upon the detection of the topic commonly mentioned in at least the predetermined number of new messages;
analyzing user data of users on a social media platform, said user data providing indications of online and real-world activity performed by the user, said analysis comprising parsing said user data based on said content of the message subset;
determining, based on said analysis, a set of users to be interested in said detected topic;
generating a new message serving as a breaking news content message comprising information associated with said common event content; and
communicating, over a network, the new message to said set of interested users.
11. The non-transitory computer-readable storage medium of claim 10 , wherein said topic detection comprises:
extracting content from each of said plurality of new messages, said extracting comprising determining a contiguous sequence of items from said content;
performing natural language processing (NLP) on the extracted content, said NLP comprises identifying stop words, where the items associated with said stop words are removed from said sequence; and
determining relationships between items associated with remaining content in each sequence that satisfy a topic threshold.
12. The non-transitory computer-readable storage medium of claim 11 , further comprising:
clustering each item satisfying the topic threshold based on said relationships, said clustered items being associated with a common category, wherein said detected topic is associated with said common category.
13. The non-transitory computer-readable storage medium of claim 10 , further comprising:
identifying a plurality of messages sent by said trusted news source via the social media platform;
parsing each of said plurality of messages from the trusted news source to identify characteristics of each message, said characteristics being said information associating the trusted news source with the content type, wherein said framing set is based on said characteristics of each message communicated by the trusted news source; and
storing said framing set in a database in association with said trusted news source.
14. The non-transitory computer-readable storage medium of claim 13 , wherein said identifying and parsing steps are performed for each identified trusted news source across a plurality of social media platforms.
15. The non-transitory computer-readable storage medium of claim 10 , wherein said trusted news source is determined to be newsworthy based on information selected from a group consisting of: the trusted news source having a number of followers or friends on the social media platform satisfying a threshold, the trusted news source's messages have been shared, quoted or forwarded a predetermined number of times during a threshold time period, the trusted news source is a verified user of a the social media platform, and the trusted news source is associated with an established media outlet.
16. The non-transitory computer-readable storage medium of claim 10 , wherein said new message is a push message, and said communication comprises facilitating sending the new message via at least one social media platform associated with each of the set of users.
17. The non-transitory computer-readable storage medium of claim 10 , wherein said communication of the new message comprises a message selected from a group consisting of: forwarding a message comprising content associated with said detected topic, quoting a message comprising content associated with said detected topic, generating a new message comprising content associated with said detected topic, and referencing a message comprising content associated with said detected topic.
18. The non-transitory computer-readable storage medium of claim 10 , wherein said plurality of new messages communicated by the trusted secondary news sources comprises social media activity occurring across a plurality of social media platforms.
19. A system comprising:
a processor; and
a non-transitory computer-readable storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising:
logic executed by the processor for determining a trusted news source from a plurality of news sources, said trust determination based on a determination that the trusted news source communicates newsworthy content via a social media platform, said trusted news source having an associated framing set comprising information associating the trusted news source with a content type;
logic executed by the processor for determining a plurality of trusted secondary news sources based on the framing set of the trusted news source, said secondary trust determination comprising parsing messages sent by the secondary news sources and identifying content in said messages that matches the framing set information at or above a threshold;
logic executed by the processor for analyzing, over a network, a plurality of new messages communicated by the trusted secondary news sources, and based on said analysis, detecting a topic commonly mentioned in at least a predetermined number of the new messages;
logic executed by the processor for identifying, over the network, a social media message respectively communicated by a plurality of users;
logic executed by the processor for filtering the social media messages communicated by the plurality of users;
logic executed by the processor for identifying, based on said filtering of the social media messages communicated by the plurality of users, a message subset, each message in the message subset comprising content associated with a common event, each message in the message subset being associated with the detected topic and communicated during a predetermined time window that commences upon the detection of the topic commonly mentioned in at least the predetermined number of new messages;
logic executed by the processor for analyzing, via the computing device, user data of users on a social media platform, said user data providing indications of online and real-world activity performed by the user, said analysis comprising parsing said user data based on said content of the message subset;
logic executed by the processor for determining, based on said analysis, a set of users to be interested in said detected topic;
logic executed by the processor for generating a new message serving as a breaking news content message comprising information associated with said common event content; and
logic executed by the processor for communicating, over a network, the new message to said set of interested users.