Granted Patent B2

US 12,223,265 · App. 17/855,248 · Granted Feb 11, 2025

Inventors: Preslav I. Nakov (Doha, QA); Giovanni Da San Martino (Doha, QA); Seunghak Yu (Doha, QA)

Assignees: QATAR FOUNDATION FOR EDUCATION, SCIENCE AND COMMUNITY DEVELOPMENT; MASSACHUSETTS INSTITUTE OF TECHNOLOGY

G06F40/205G06F3/04842G06F40/289

Abstract

Claims (132)

1. A method, comprising:

parsing each sentence of a plurality of sentences in an article for structural details, wherein the plurality of sentences represent a threshold number of sentences selected from a beginning of the article;

identifying, via a machine learning model, dimensional features across the plurality of sentences based on the structural details;

identifying, via the machine learning model, a propagandizing tactic demonstrated in each sentence of the plurality of sentences based on a covariance score to the propagandizing tactic and the dimensional features identified for a given sentence; and

displaying, in a user interface that includes the article, propaganda labels corresponding to the propagandizing tactic identified for each sentence of the plurality of sentences.

2. The method of claim 1 , wherein the propagandizing tactic is selected by the machine learning model from a list comprising:

name calling;

repetition;

slogans;

appeal to fear;

doubt;

exaggeration;

flag-waving;

loaded language;

association with an out-group;

bandwagon;

casual oversimplification;

obfuscation;

appeal to authority;

binary fallacy;

thought-terminating clichés;

red herrings;

strawman arguments;

whataboutisms; and

none.

3. The method of claim 2 , further comprising, in response to receiving a veracity selection from a user to include the veracity of information included in the sentence when identifying the propagandizing tactic demonstrated in each sentence of the plurality of sentences:

identifying whether statements included in each of the sentences of the plurality of sentences are true or false;

categorizing any sentences of the plurality of sentences that includes a false statement demonstrating a lying propagandizing tactic; and

selecting the propagandizing tactic for any sentences of the plurality of sentences that exclude false statements from the list.

4. The method of claim 2 , further comprising, in response to receiving a veracity selection from a user to ignore the veracity of information included in the sentence when identifying the propagandizing tactic demonstrated in each sentence of the plurality of sentences:

categorizing false statements identically to true statements when selecting the propagandizing tactic from the list.

5. The method of claim 1 , wherein the dimensional features include:

a relative position of a given sentence in the article relative to other sentences;

a semantic stance of the given sentence relative to a title of the article;

a sentiment of the given sentence; and

an article-level prediction of the article as a whole being propaganda.

6. The method of claim 5 , wherein the semantic stance is selected from a group including:

related agreeing;

related disagreeing;

related discussing; and

unrelated.

7. The method of claim 5 , wherein the sentiment is selected from a group including:

positive;

negative;

neutral; and

compound.

8. A system, comprising:

a processor; and

a memory, that includes instructions that when executed by the processor perform operations, including:

parsing each sentence of a plurality of sentences in an article for structural details, wherein the plurality of sentences represent a threshold number of sentences selected from a beginning of the article;

identifying, via a machine learning model, dimensional features across the plurality of sentences based on the structural details;

identifying, via the machine learning model, a propagandizing tactic demonstrated in each sentence of the plurality of sentences based on a covariance score to the propagandizing tactic and the dimensional features identified for a given sentence; and

displaying, in a user interface that includes the article, propaganda labels corresponding to the propagandizing tactic identified for each sentence of the plurality of sentences.

9. The system of claim 8 , wherein the propagandizing tactic is selected by the machine learning model from a list comprising:

name calling;

repetition;

slogans;

appeal to fear;

doubt;

exaggeration;

flag-waving;

loaded language;

association with an out-group;

bandwagon;

casual oversimplification;

obfuscation;

appeal to authority;

binary fallacy;

thought-terminating clichés;

red herrings;

strawman arguments;

whataboutisms; and

none.

10. The system of claim 9 , further comprising, in response to receiving a veracity selection from a user to ignore the veracity of information included in the sentence when identifying the propagandizing tactic demonstrated in each sentence of the plurality of sentences:

categorizing false statements identically to true statements when selecting the propagandizing tactic from the list.

11. The system of claim 8 , wherein the dimensional features include:

a relative position of a given sentence in the article relative to other sentences;

a semantic stance of the given sentence relative to a title of the article;

a sentiment of the given sentence; and

an article-level prediction of the article as a whole being propaganda.

12. The system of claim 11 , wherein the semantic stance is selected from a group including:

related agreeing;

related disagreeing;

related discussing; and

unrelated.

13. The system of claim 11 , wherein the sentiment is selected from a group including:

positive;

negative;

neutral; and

compound.

14. A memory apparatus that includes instructions that when executed by a processor perform operations, comprising:

parsing each sentence of a plurality of sentences in an article for structural details, wherein the plurality of sentences represent a threshold number of sentences selected from a beginning of the article;

identifying, via a machine learning model, dimensional features across the plurality of sentences based on the structural details;

identifying, via the machine learning model, a propagandizing tactic demonstrated in each sentence of the plurality of sentences based on a covariance score to the propagandizing tactic and the dimensional features identified for a given sentence; and

displaying, in a user interface that includes the article, propaganda labels corresponding to the propagandizing tactic identified for each sentence of the plurality of sentences.

15. The memory apparatus of claim 14 , wherein the propagandizing tactic is selected by the machine learning model from a list comprising:

name calling;

repetition;

slogans;

appeal to fear;

doubt;

exaggeration;

flag-waving;

loaded language;

association with an out-group;

bandwagon;

casual oversimplification;

obfuscation;

appeal to authority;

binary fallacy;

thought-terminating clichés;

red herrings;

strawman arguments;

whataboutisms; and

none.

16. The memory apparatus of claim 15 , the operations further comprising, in response to receiving a veracity selection from a user to ignore the veracity of information included in the sentence when identifying the propagandizing tactic demonstrated in each sentence of the plurality of sentences:

categorizing false statements identically to true statements when selecting the propagandizing tactic from the list.

17. The memory apparatus of claim 14 , wherein the dimensional features include:

a relative position of a given sentence in the article relative to other sentences;

a semantic stance of the given sentence relative to a title of the article;

a sentiment of the given sentence; and

an article-level prediction of the article as a whole being propaganda.

18. The memory apparatus of claim 17 , wherein the semantic stance is selected from a group including:

related agreeing;

related disagreeing;

related discussing; and

unrelated.

19. The memory apparatus of claim 17 , wherein the sentiment is selected from a group including:

positive;

negative;

neutral; and

compound.

Assignments (3)

Continuity (2)

References Cited (19)