Proposed compartmentalized architecture for Context-Sensitive Stance Classification.
<p>The white arrow denotes the starting input for each component. The left figure presents the Tweet Level encoding applied to a batch of <i>n</i><sub><i>max</i></sub> tweets from a given user producing a vector of dimensions () per user. For the <i>Tw...
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2025
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| _version_ | 1852018964849229824 |
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| author | Ramon Villa-Cox (8311131) |
| author2 | Evan M. Williams (21609012) Kathleen M. Carley (2636068) |
| author2_role | author author |
| author_facet | Ramon Villa-Cox (8311131) Evan M. Williams (21609012) Kathleen M. Carley (2636068) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ramon Villa-Cox (8311131) Evan M. Williams (21609012) Kathleen M. Carley (2636068) |
| dc.date.none.fl_str_mv | 2025-06-26T17:56:25Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0324697.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Proposed_compartmentalized_architecture_for_Context-Sensitive_Stance_Classification_/29419917 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Geology Cancer Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified including opinion polling dimensional space using detection models using stance detection models new country contexts different country contexts user &# 8217 predict user stances political stance detection extrapolate stance predictions country model performance impact social applications extrapolation </ p political stance every country based user social context extrapolation power wide range tweet encoders scale weakly important task hate speech future events existing large embed users detecting propaganda construct transformer |
| dc.title.none.fl_str_mv | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The white arrow denotes the starting input for each component. The left figure presents the Tweet Level encoding applied to a batch of <i>n</i><sub><i>max</i></sub> tweets from a given user producing a vector of dimensions () per user. For the <i>Tweet Embeddings</i> to serve as input for the User Transformer (depicted in the middle panel) they are reshaped and a [<i>CLS</i>] parameter vector is appended at the start of each user’s tweets. In this layer, a batch of <i>b</i><sub><i>size</i></sub> users is processed by <i>L</i><sub><i>x</i></sub> encoder stacks obtaining the final <i>User Embeddings</i>. On the right, we show the heterogeneous GAT model, wherein a user receives information from its neighborhood and their corresponding <i>User Embeddings</i>. The attention mechanism learns different attention weights for which reflect the importance of a neighbor <i>j</i> to the label of the <i>i</i>’th user.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_e4d73cdcd9c766b3fd43dcbcfe212662 |
| identifier_str_mv | 10.1371/journal.pone.0324697.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29419917 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Proposed compartmentalized architecture for Context-Sensitive Stance Classification.Ramon Villa-Cox (8311131)Evan M. Williams (21609012)Kathleen M. Carley (2636068)GeologyCancerScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedincluding opinion pollingdimensional space usingdetection models usingstance detection modelsnew country contextsdifferent country contextsuser &# 8217predict user stancespolitical stance detectionextrapolate stance predictionscountry model performanceimpact social applicationsextrapolation </ ppolitical stanceevery countrybased usersocial contextextrapolation powerwide rangetweet encodersscale weaklyimportant taskhate speechfuture eventsexisting largeembed usersdetecting propagandaconstruct transformer<p>The white arrow denotes the starting input for each component. The left figure presents the Tweet Level encoding applied to a batch of <i>n</i><sub><i>max</i></sub> tweets from a given user producing a vector of dimensions () per user. For the <i>Tweet Embeddings</i> to serve as input for the User Transformer (depicted in the middle panel) they are reshaped and a [<i>CLS</i>] parameter vector is appended at the start of each user’s tweets. In this layer, a batch of <i>b</i><sub><i>size</i></sub> users is processed by <i>L</i><sub><i>x</i></sub> encoder stacks obtaining the final <i>User Embeddings</i>. On the right, we show the heterogeneous GAT model, wherein a user receives information from its neighborhood and their corresponding <i>User Embeddings</i>. The attention mechanism learns different attention weights for which reflect the importance of a neighbor <i>j</i> to the label of the <i>i</i>’th user.</p>2025-06-26T17:56:25ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0324697.g001https://figshare.com/articles/figure/Proposed_compartmentalized_architecture_for_Context-Sensitive_Stance_Classification_/29419917CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294199172025-06-26T17:56:25Z |
| spellingShingle | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. Ramon Villa-Cox (8311131) Geology Cancer Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified including opinion polling dimensional space using detection models using stance detection models new country contexts different country contexts user &# 8217 predict user stances political stance detection extrapolate stance predictions country model performance impact social applications extrapolation </ p political stance every country based user social context extrapolation power wide range tweet encoders scale weakly important task hate speech future events existing large embed users detecting propaganda construct transformer |
| status_str | publishedVersion |
| title | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. |
| title_full | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. |
| title_fullStr | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. |
| title_full_unstemmed | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. |
| title_short | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. |
| title_sort | Proposed compartmentalized architecture for Context-Sensitive Stance Classification. |
| topic | Geology Cancer Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified including opinion polling dimensional space using detection models using stance detection models new country contexts different country contexts user &# 8217 predict user stances political stance detection extrapolate stance predictions country model performance impact social applications extrapolation </ p political stance every country based user social context extrapolation power wide range tweet encoders scale weakly important task hate speech future events existing large embed users detecting propaganda construct transformer |