Systems and methods for processing electronic content
Systems and methods are disclosed for processing electronic content, such as text, videos, and images. According to certain embodiments, user interactions with electronic content may be tracked over a plurality of modalities, such as web pages, email, mobile applications, and social media. The tracked user interactions may include copy/paste events, explicit user highlighting, social sharing, and user voting. Key passages of electronic content may be identified based on the tracked user interactions and ranked against one another. Ranking of passages may be based, for example, on a raw or normalized score for the identified key passages. Alternatively, the ranking of a passage may be based on a ratio of user interactions with the passage to total views of the electronic text containing the passage. One or more of the identified key passages (e.g., the highest ranked passages) may be published to one or more applications.
1. A content filtering system for filtering one or more electronic content elements for distribution to one or more electronic applications, the system comprising:
a database storing instructions for filtering one or more electronic content items; and
one or more processors configured to execute the instructions to perform a method, the method including:
receiving, by a first server processor, one or more electronic content items from a second server processor in electronic communication with the first server processor, the second server processor associated with at least one of a web server, an email server, a mobile application server, and a social media server;
tracking, by the first server processor, one or more user interactions of a first user with the one or more electronic content items, wherein the first user interacts with the one or more electronic content items across one or more modalities of electronic publishing content;
receiving, by the first server processor, indication of one or more user interactions by the first user with the one or more electronic content items;
based on a machine learning model, determining, by the first server processor, one or more key passages of the one or more electronic content items that the first user interacted with;
determining, by the server processor, a ranking order of the one or more key passages;
updating, by the first server processor, a user profile associated with the first user to include one of the one more user interactions and the one or more key passages; and
based on the updated user profile and the ranking order of the one or more key passages, transmitting, by the first server processor, electronic content information to one or more computer applications associated with the first user.
2. The system of claim 1 , further comprising:
assigning a score to user interactions with the one or more electronic content items.
3. The system of claim 2 , wherein assigning a score to a user interaction further comprises:
determining how the first user interacted with the one or more electronic content items; and
weighting user interactions according to a predetermined scale.
4. The system of claim 2 , wherein assigning a score to user interactions further comprises:
generating a raw score for the one or more electronic content items based on aggregating the scores assigned to the user interactions.
5. The system of claim 1 , wherein the user profile is updated further comprising:
receiving a notification from the first user indicating whether the first user has requested the one or more user interactions to be associated with the user profile.
6. The system of claim 4 , further comprising:
performing a behavior analysis based on the user interactions and a type of electronic content interacted with by the first user and comparing the user profile to one or more stored profiles of other users; and
identifying one or more users with similar behavioral interests based on the behavioral analysis.
7. The system of claim 6 , further comprising:
recirculating the electronic content with the highest raw scores to the identified one or more users with similar behavioral interest as the user.
8. A computer-implemented method for filtering one or more electronic content items, the method comprising:
receiving, by a first server processor, one or more electronic content items from a second server processor in electronic communication with the first server processor, the second server processor associated with at least one of a web server, an email server, a mobile application server, and a social media server;
tracking, by the first server processor, one or more user interactions of a first user with the one or more electronic content items, wherein the first user interacts with the one or more electronic content items across one or more modalities of electronic publishing content;
receiving, by the first server processor, indication of one or more user interactions by the first user with the one or more electronic content items;
based on a machine learning model, determining, by the first server processor, one or more key passages of the one or more electronic content items that the first user interacted with;
determining, by the server processor, a ranking order of the one or more key passages;
updating, by the first server processor, a user profile associated with the first user to include one of the one more user interactions and the one or more key passages; and
based on the updated user profile and the ranking order of the one or more key passages, transmitting, by the first server processor, electronic content information to one or more computer applications associated with the first user.
9. The method of claim 8 , further comprising:
assigning a score to user interactions with the one or more electronic content items.
10. The method of claim 9 , wherein assigning a score to a user interaction further comprises:
determining how the first user interacted with the one or more electronic content items; and
weighting user interactions according to a predetermined scale.
11. The method of claim 9 , wherein assigning a score to user interactions further comprises:
generating a raw score for the one or more electronic content items based on aggregating the scores assigned to the user interactions.
12. The method of claim 8 , wherein the user profile is updated further comprising:
receiving a notification from the first user indicating whether the first user has requested the one or more user interactions to be associated with the user profile.
13. The method of claim 11 , further comprising:
performing a behavior analysis based on the user interactions and a type of electronic content interacted with by the first user and comparing the user profile to one or more stored profiles of other users; and
identifying one or more users with similar behavioral interests based on the behavioral analysis.
14. The method of claim 13 , further comprising:
recirculating the one or more electronic content items with the highest raw scores to the identified one or more users with similar behavioral interest as the first user.
15. A non-transitory computer-readable medium storing instructions for filtering one or more electronic content elements for distribution to one or more electronic applications, the instructions configured to cause at least one processor to perform operations, the operations including:
receiving, by a first server processor, one or more electronic content items from a second server processor in electronic communication with the first server processor, the second server processor associated with at least one of a web server, an email server, a mobile application server, and a social media server;
tracking, by the first server processor, one or more user interactions of a first user with the one or more electronic content items, wherein the first user interacts with the one or more electronic content items across one or more modalities of electronic publishing content;
receiving, by the first server processor, indication of one or more user interactions by the first user with the one or more electronic content items;
based on a machine learning model, determining, by the first server processor, one or more key passages of the one or more electronic content items that the first user interacted with;
determining, by the server processor, a ranking order of the one or more key passages;
updating, by the first server processor, a user profile associated with the first user to include one of the one more user interactions and the one or more key passages; and
based on the updated user profile and the ranking order of the one or more key passages, transmitting, by the first server processor, electronic content information to one or more computer applications associated with the first user.
16. The computer readable medium of claim 15 , further comprising:
assigning a score to user interactions with the one or more electronic content items.
17. The computer readable medium of claim 16 , wherein assigning a score to a user interaction further comprises:
determining how the first user interacted with the one or more electronic content items; and
weighting user interactions according to a predetermined scale.
18. The computer readable medium of claim 16 , wherein assigning a score to user interactions further comprises:
generating a raw score for the one or more electronic content items based on aggregating the scores assigned to the user interactions.
19. The computer readable medium of claim 18 , further comprising:
performing a behavior analysis based on the user interactions and a type of electronic content interacted with by the first user and comparing the user profile to one or more stored profiles of other users; and
identifying one or more users with similar behavioral interests based on the behavioral analysis.
20. The computer readable medium of claim 19 , further comprising:
recirculating the one or more electronic content items with the highest raw scores to the identified one or more users with similar behavioral interest as the first user.