Automated measurement of content quality
A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.
1. A system comprising:
a processor, and a memory including instructions, which when executed by the processor, cause the processor to:
generate a first graph comprising a user node and an article node, the user node uniquely corresponds to a user and the article node uniquely corresponds to an article;
generate an edge between the user node and the article node in the first graph based on a first action type by the user in association with the article;
generate a second graph comprising the user node and the article node;
generate an edge between the user node and the article node in the second graph based on a second action type by the user in association with the article;
calculate a first authority score for the article node within the first graph based on a hub score of the user node in the first graph;
calculate a second authority score for the article node within the second graph based on a hub score of the user node in the second graph; and
calculate a quality score for the article based on a weighted average of the first authority score and the second authority score;
rank the article among other articles based on the quality score; and
display the article in a graphical user interface based on the ranking.
2. The system of claim 1 , further comprising:
determine a weight for each node based on a logistic regression model using data collected from user engagement with articles within a browsing session; and
the weighted average is based on the determined weight for each node.
3. The system of claim 1 , wherein:
the first graph comprises a plurality of user nodes;
generate edges between the plurality of user nodes and the article node within the first graph; and
the calculating the first authority score based on a normalized su of the hubs scores corresponding to the plurality of the user nodes.
4. The system of claim 1 , wherein:
the graph includes a second user node, the second user node corresponds to a second user; and
generate an edge between the user node and the second user node based on an action by the user in association with the second user.
5. The system of claim 1 , wherein:
the graph includes a second article node, the second article node corresponds to a second article; and
generate an edge between the article node and the second article node based on a hyperlink within the article that corresponds to the second article.
6. The system of claim 1 , wherein:
the calculating the first authority score for the article node within the first graph and the second authority score within the second graph is based on a stochastic approach for linked-structure analysis.
7. The system of claim 1 , wherein:
the first action type is an action selected from a group consisting of sharing, commenting, liking, dwelling, or clicking on the article.
8. The system of claim 1 , wherein:
the first graph comprises a plurality of article nodes;
determine that an article node within the plurality of article nodes has no connecting edges; and
calculate a quality score for the article node within thy: plurality of article nodes based on an authority score corresponding to the web domain associated with the article node.
9. A method comprising:
using one or more computer processors:
generating a first graph comprising a user node and an article node, the user node uniquely corresponds to a user and the article node uniquely corresponds to an article;
generating an edge between the user node and the article node in the first graph based on a first action type by the user in association with the article;
generating a second graph comprising the user node and the article node;
generating an edge between the user node and the article node in the second graph based on a second action type by the user in association with the article;
calculating a first authority score for the article node within the first graph based on a hub score of the user node in the first graph;
calculating a second authority score for the article node within the second graph based on a hub score of the user node in the second graph; and
calculating a quality score for the article based on a weighted average of the first authority score and the second authority score;
ranking the article among other articles based on the quality score; and
displaying the article in a graphical user interface based on the ranking.
10. The method of claim 9 , further comprising:
determining a weight for each node based on a logistic regression model using data collected from user engagement with articles within a browsing session; and
the weighted average is based on the determined weight for each node.
11. The method of claim 9 , wherein:
the first graph comprises a plurality of user nodes;
generating edges between the plurality of user nodes and the article node within the first graph; and
the calculating the first authority score based on a normalized sum of the hubs scores corresponding to the plurality of the user nodes.
12. The method of claim 9 , wherein:
the first graph includes a second user node, the second user node corresponds to a second user; and
generating an edge between the user node and the second user node based on an action by the user in association with the second user.
13. The method of claim 9 , wherein:
the first graph includes a second article node, the second article node corresponds to a second article; and
generating an edge between the article node and the second article node based on a hyperlink within the article that corresponds to the second article.
14. The method of claim 9 , wherein:
the calculating the first authority score for the article node within the first graph and the second authority score within the second graph is based on a stochastic approach for linked-structure analysis.
15. The method of claim 9 , wherein:
the first action type is an action selected from a group consisting of sharing, commenting, liking, dwelling, or clicking on the article.
16. The method of claim 9 , wherein
the first graph comprises a plurality of article nodes;
determine that an article node within the plurality of article nodes has no connecting edges; and
calculating a quality score for the article node within the plurality of article nodes based on an authority score corresponding to the web domain associated with the article node.
17. A machine-readable medium not having any transitory signals and storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:
generating a first graph comprising a user node and an article node, the user node uniquely corresponds to a user and the article node uniquely corresponds to an article;
generating an edge between the user node and the article node in the first graph based on a first action type by the user in association with the article;
generating a second graph comprising the user node and the article node;
generating an edge between the user node and the article node in the second graph based on a second action type by the user in association with the article;
calculating a first authority score for the article node within the first graph based on a hub score of the user node in the first graph;
calculating a second authority score for the article node within the second graph based on a hub score of the user node in the second graph; and
calculating a quality score for the article based on a weighted average of the first authority score and the second authority score;
ranking the article among other articles based on the quality score; and
displaying the article in a graphical user interface based on the ranking.
18. The machine-readable medium of claim 17 , wherein the operations further comprise:
determining a weight for each node based on a logistic regression model using data collected from user engagement with articles within a browsing session; and
the weighted average is based on the determined weight for each node.
19. The machine-readable medium of claim 17 , wherein:
the first graph includes a second user node, the second user node corresponds to a second user; and
wherein the operations further comprise generating an edge between the user node and the second user node based on an action by the user in association with the second user.
20. The machine-readable medium of claim 17 , wherein:
the first graph comprises a plurality of article nodes;
determine that an article node within the plurality of article nodes has no connecting edges; and
wherein the operations further comprise calculating a quality score for the article node within the plurality of article nodes based on an authority score corresponding to the web domain associated with the article node.