Automated Hashtag Hierarchy Generation Using Community Detection and the Shannon Diversity Index
Developing semantic hierarchies from user-created hashtags in social media can provide useful organizational structure to large volumes of data. However, construction of these hierarchies is difficult using established ontologies (e.g. WordNet [1]) due to the differences in the semantic and pragmati...
Uloženo v:
| Vydáno v: | 2022 IEEE 16th International Conference on Semantic Computing (ICSC) s. 59 - 66 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.01.2022
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Developing semantic hierarchies from user-created hashtags in social media can provide useful organizational structure to large volumes of data. However, construction of these hierarchies is difficult using established ontologies (e.g. WordNet [1]) due to the differences in the semantic and pragmatic use of words vs. hashtags in social media. While alternative construction methods based on hashtag frequency are relatively straightforward, these methods can be susceptible to the dynamic nature of social media, such as hashtags associated with surges in popularity. We drew inspiration from the ecologically-based Shannon Diversity Index (SDI) [2] to create a more representative and resilient method of semantic hierarchy construction that relies upon graph-based community detection and a novel, entropy-based ensemble diversity index (EDI) score. The EDI quantifies the contextual diversity of each hashtag, resulting in thousands of semantically-related groups of hashtags organized along a general-to-specific spectrum. Through an application of EDI to Twitter data and a comparison of our results to prior approaches, we demonstrate our method's ability to create semantically consistent hierarchies that can be flexibly applied and adapted to a range of use cases. |
|---|---|
| AbstractList | Developing semantic hierarchies from user-created hashtags in social media can provide useful organizational structure to large volumes of data. However, construction of these hierarchies is difficult using established ontologies (e.g. WordNet [1]) due to the differences in the semantic and pragmatic use of words vs. hashtags in social media. While alternative construction methods based on hashtag frequency are relatively straightforward, these methods can be susceptible to the dynamic nature of social media, such as hashtags associated with surges in popularity. We drew inspiration from the ecologically-based Shannon Diversity Index (SDI) [2] to create a more representative and resilient method of semantic hierarchy construction that relies upon graph-based community detection and a novel, entropy-based ensemble diversity index (EDI) score. The EDI quantifies the contextual diversity of each hashtag, resulting in thousands of semantically-related groups of hashtags organized along a general-to-specific spectrum. Through an application of EDI to Twitter data and a comparison of our results to prior approaches, we demonstrate our method's ability to create semantically consistent hierarchies that can be flexibly applied and adapted to a range of use cases. |
| Author | Howald, Blake Torene, Spencer |
| Author_xml | – sequence: 1 givenname: Spencer surname: Torene fullname: Torene, Spencer email: spencer.torene@trssllc.com organization: Thomson Reuters Special Services, LLC,McLean,VA,USA – sequence: 2 givenname: Blake surname: Howald fullname: Howald, Blake email: blake.howald@trssllc.com organization: Thomson Reuters Special Services, LLC,McLean,VA,USA |
| BookMark | eNotjkFOwzAURI0ECyg9ASx8gQR_20nsZZVCU6kSi9J1cZzvxhJxUOIicntCYTUjzdPo3ZHr0Ack5BFYCsD007bclxlXElLOOE8ZY5BfkaUuFOR5JoUEpW7J--oc-85EbGhlxjaaE608Dmaw7UQ3GOYafR_oYfThRMu-687Bx4muMaK9LCY0NLZI960JswJd-y8cxl9mGxr8vic3znyMuPzPBTm8PL-VVbJ73WzL1S7xIERMJGTINIosU6pGC6CknUWR59wWzpkclFaoAMEoWTjpaqMtA6mtcLVCKxbk4e_XI-Lxc_CdGaajLkTONRM_0IhTNA |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICSC52841.2022.00016 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library (IEL) (UW System Shared) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781665434188 166543418X |
| EndPage | 66 |
| ExternalDocumentID | 9736290 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i133t-415e09e35588bec1184c816e262c7ffa61898e81e1a847f4fba9c0149c3fb8ec3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000835706300008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Thu Jun 29 18:36:55 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i133t-415e09e35588bec1184c816e262c7ffa61898e81e1a847f4fba9c0149c3fb8ec3 |
| PageCount | 8 |
| ParticipantIDs | ieee_primary_9736290 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-Jan. |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-Jan. |
| PublicationDecade | 2020 |
| PublicationTitle | 2022 IEEE 16th International Conference on Semantic Computing (ICSC) |
| PublicationTitleAbbrev | ICSC |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8024256 |
| Snippet | Developing semantic hierarchies from user-created hashtags in social media can provide useful organizational structure to large volumes of data. However,... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 59 |
| SubjectTerms | Indexes information entropy recommender systems Refining Semantics social computing Social networking (online) tagging text analysis Time series analysis Training data Weight measurement |
| Title | Automated Hashtag Hierarchy Generation Using Community Detection and the Shannon Diversity Index |
| URI | https://ieeexplore.ieee.org/document/9736290 |
| WOSCitedRecordID | wos000835706300008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NTwIxEG2QePCkBozf6cGjK-wH2_ZoQAKJISSo4YbTdipcFgLFhH9v213RgxdvTS9Npkln3vS9N4TcGRDSVSGe1SjBd6vSCLKsE3HdllxqBSWr8u2ZjUZ8OhXjGrnfa2EQMZDP8MEvw1--Xqqtb5W1BHPPrXAA_YCxvNRqVWq4uC1aw-6k23GvrUd9SbDh9EPMf81MCSmjf_y_w05I80d7R8f7rHJKalg0yPvj1i5dbYmaDmAzt_BBBwsvHVbzHS2do32AaSAA0Er0YXe0hzZQrQoKhaau1qOTORQO8dPeNx-DDr1fYpO89p9euoOomo0QLRyqtJHLu9gW6M3RubsGBxMyxeMckzxRzBjIYy448hhjcPnHZEaCUB4OqdRIjio9I3V3Gp4TmkKmjNYdj82yhCmQgutYoQHtwAmDC9Lw0ZmtSvuLWRWYy7-3r8iRD3_Zpbgmdbve4g05VJ92sVnfhjv7ApW_m64 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT8JAEN0QNNGTGjCKX3vwaIV-0d2jAUmJSEhAww2nu7PCpRgoJvx7d9qKHrx4a3rZZCbZmTf73hvGbg3IxHYhxGpMgKZVvgNBEDpCtxKRaAUFq_J1EA2HYjqVowq722lhEDEnn-E9feZv-XqpNjQqa8rIXrfSAvQ92pwVFmqtUg_ntmSz3xl3QnvfEu7zciNOWmP-a2tKXjR6R_877pjVf9R3fLSrKyesgmmNvT1ssqXtLlHzGNbzDN55vCDxsJpveeEdTSHmOQWAl7KPbMu7mOVkq5RDqrnt9vh4DqnF_Lz7zcjgfXJMrLOX3uOkEzvldgRnYXFl5tgAYEsi2aMLmwgLFAIl3DZ6bU9FxkDbFVKgcNEFW4FMYBKQigCR8k0iUPmnrGpPwzPGfQiU0TokdBZ4kYJECu0qNKAtPIngnNUoOrOPwgBjVgam8ffvG3YQT54Hs0F_-HTBDikVxcziklWz1Qav2L76zBbr1XWevy_iTJ75 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+IEEE+16th+International+Conference+on+Semantic+Computing+%28ICSC%29&rft.atitle=Automated+Hashtag+Hierarchy+Generation+Using+Community+Detection+and+the+Shannon+Diversity+Index&rft.au=Torene%2C+Spencer&rft.au=Howald%2C+Blake&rft.date=2022-01-01&rft.pub=IEEE&rft.spage=59&rft.epage=66&rft_id=info:doi/10.1109%2FICSC52841.2022.00016&rft.externalDocID=9736290 |