SYSTEM AND METHOD FOR DISPLAYING AND FILTERING MEDIA CONTENT IN A MESSAGING CLIENT
In some aspects, the techniques described herein relate to a method including: receiving a set of messages; filtering the set of messages to identify a set of newsletters in response to a selection of a newsletter control within a messaging application; rendering a newsletter view within the messaging application, the newsletter view displaying a plurality of tiles corresponding to the set of newsletters; receiving a section of a given tile in the plurality of tiles from a user; and rendering a newsletter reader view, the newsletter reader view including a subject and body of a selected newsletter corresponding to the given tile.
1 - 20 . (canceled)
21 . A method comprising:
displaying, via a messaging application executing on a computing device, a newsletter in a reader view, the reader view including a subject and body of the newsletter;
computing a reading progress of the newsletter by determining a position of a last displayed word within the body and calculating a progress percentage based on the position of the last displayed word relative to a total word count of the newsletter;
determining an end of the newsletter when the reading progress reaches a predetermined threshold percentage; and
in response to reaching the end, automatically displaying an adjacent message details area that includes one or more of an icon of a sender of an adjacent newsletter, a subject line of the adjacent newsletter, and an indicator identifying whether the adjacent newsletter precedes or follows the newsletter.
22 . The method of claim 21 , further comprising classifying messages as newsletters using a machine learning model trained on a dataset of previously labeled messages.
23 . The method of claim 22 , wherein the machine learning model comprises a multi-label classifier capable of assigning one of a fixed set of labels to a message.
24 . The method of claim 21 , further comprising: applying a topic classification to the newsletter using a topic classifier; and rendering a category tag for the newsletter based on the topic classification.
25 . The method of claim 21 , wherein computing the reading progress further comprises: removing stop words from the body before calculating a word count; and applying natural language processing techniques to determine the position of the last displayed word.
26 . The method of claim 21 , further comprising: maintaining a saved flag associated with the newsletter; and updating the saved flag based on user interactions with the newsletter in the reader view.
27 . The method of claim 21 , further comprising: maintaining a discovery view within the messaging application; and populating the discovery view with newsletter recommendations based on one or more of newsletters previously opened by a user and newsletters saved to a reading list of the user.
28 . The method of claim 21 , further comprising rendering a progress bar in the reader view representing the reading progress, wherein the progress bar is dynamically updated in real-time as a user scrolls through the newsletter.
29 . A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
displaying, via a messaging application executing on a computing device, a newsletter in a reader view, the reader view including a subject and body of the newsletter;
computing a reading progress of the newsletter by determining a position of a last displayed word within the body and calculating a progress percentage based on the position of the last displayed word relative to a total word count of the newsletter;
determining an end of the newsletter when the reading progress reaches a predetermined threshold percentage; and
in response to reaching the end, automatically displaying an adjacent message details area that includes one or more of an icon of a sender of an adjacent newsletter, a subject line of the adjacent newsletter, and an indicator identifying whether the adjacent newsletter precedes or follows the newsletter.
30 . The non-transitory computer-readable storage medium of claim 29 , the steps further comprising classifying messages as newsletters using a machine learning model trained on a dataset of previously labeled messages.
31 . The non-transitory computer-readable storage medium of claim 30 , wherein the machine learning model comprises a multi-label classifier capable of assigning one of a fixed set of labels to a message.
32 . The non-transitory computer-readable storage medium of claim 29 , the steps further comprising: applying a topic classification to the newsletter using a topic classifier; and rendering a category tag for the newsletter based on the topic classification.
33 . The non-transitory computer-readable storage medium of claim 29 , wherein computing the reading progress further comprises: removing stop words from the body before calculating a word count; and applying natural language processing techniques to determine the position of the last displayed word.
34 . The non-transitory computer-readable storage medium of claim 29 , the steps further comprising: maintaining a saved flag associated with the newsletter; and updating the saved flag based on user interactions with the newsletter in the reader view.
35 . The non-transitory computer-readable storage medium of claim 29 , the steps further comprising: maintaining a discovery view within the messaging application; and populating the discovery view with newsletter recommendations based on one or more of newsletters previously opened by a user and newsletters saved to a reading list of the user.
36 . The non-transitory computer-readable storage medium of claim 29 , the steps further comprising rendering a progress bar in the reader view representing the reading progress, wherein the progress bar is dynamically updated in real-time as a user scrolls through the newsletter.
37 . A device comprising:
a processor; and
a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic including steps for:
displaying, via a messaging application executing on the processor, a newsletter in a reader view, the reader view including a subject and body of the newsletter;
computing a reading progress of the newsletter by determining a position of a last displayed word within the body and calculating a progress percentage based on the position of the last displayed word relative to a total word count of the newsletter;
determining an end of the newsletter when the reading progress reaches a predetermined threshold percentage; and
in response to reaching the end, automatically displaying an adjacent message details area that includes one or more of an icon of a sender of an adjacent newsletter, a subject line of the adjacent newsletter, and an indicator identifying whether the adjacent newsletter precedes or follows the newsletter.
38 . The device of claim 37 , the steps further comprising classifying messages as newsletters using a machine learning model trained on a dataset of previously labeled messages.
39 . The device of claim 38 , wherein the machine learning model comprises a multi-label classifier capable of assigning one of a fixed set of labels to a message.
40 . The device of claim 37 , the steps further comprising: applying a topic classification to the newsletter using a topic classifier; and rendering a category tag for the newsletter based on the topic classification.