Neural age screening on question answering communities.
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| Title: | Neural age screening on question answering communities. |
|---|---|
| Authors: | Timilsina, Mohan1 (AUTHOR) mohan.timilsina@insight-centre.org, Figueroa, Alejandro2 (AUTHOR) alejandro.figueroa@unab.cl |
| Source: | Engineering Applications of Artificial Intelligence. Aug2023:Part A, Vol. 123, pN.PAG-N.PAG. 1p. |
| Subjects: | Virtual communities, Communities, Online social networks, Standard deviations |
| Abstract: | For online social networks, demographic analysis is absolutely essential for improving their services in many ways. It is instrumental in understanding their different audiences, members and competitors. As well as that, it is pivotal in designing effective personalization and contextualization strategies, especially for displaying and creating better content. There is, for this reason, a great bulk of research into how demographic variables are characterized and how they impact online platforms such as Facebook and Twitter. But surprisingly, only a handful of works delve into their characterization and effect on community Question-Answering (cQA) websites. In this particular context, the subject of age demographics remains largely unexplored. This paper takes the lead on interpreting automatic age recognition on CQAs (a.k.a. age screening) as a regression task. To this effect, it compares state-of-the-art graph-based neural network regression and embedding models on a massive activity-graph encompassing ca. 16 and 837 million nodes (members) and edges, respectively. For this study, a large-scale subset of ca. 657,000 community fellows was automatically associated with their age via aligning their profile texts with a limited number of linguistic patterns. In short, our results show that Node2vec significantly outperforms other embeddings regardless of the regression model used for casting predictions. When this embedding is combined with Artificial Neural Network Regressions, we obtained our best configuration scoring a Root Mean Square Error (RMSE) of 8.39. An interesting qualitative feature of this embedding space is that age-based centroid vectors tend to form a trail ordered by age. Lastly, our outcomes also signal that activity graph based models can rival its counterparts based on image and textual inputs, paving the way for constructing effective multi-modal approaches. • We conducted age demographic analysis on a massive activity graph. • Several graph-based neural network regression models were compared. • Age-based centroid vectors tend to form a trail ordered by age. • Graph models can rival its counterparts based on image and textual inputs. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 163976103 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Neural age screening on question answering communities. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Timilsina%2C+Mohan%22">Timilsina, Mohan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mohan.timilsina@insight-centre.org</i><br /><searchLink fieldCode="AR" term="%22Figueroa%2C+Alejandro%22">Figueroa, Alejandro</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> alejandro.figueroa@unab.cl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Aug2023:Part A, Vol. 123, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Virtual+communities%22">Virtual communities</searchLink><br /><searchLink fieldCode="DE" term="%22Communities%22">Communities</searchLink><br /><searchLink fieldCode="DE" term="%22Online+social+networks%22">Online social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: For online social networks, demographic analysis is absolutely essential for improving their services in many ways. It is instrumental in understanding their different audiences, members and competitors. As well as that, it is pivotal in designing effective personalization and contextualization strategies, especially for displaying and creating better content. There is, for this reason, a great bulk of research into how demographic variables are characterized and how they impact online platforms such as Facebook and Twitter. But surprisingly, only a handful of works delve into their characterization and effect on community Question-Answering (cQA) websites. In this particular context, the subject of age demographics remains largely unexplored. This paper takes the lead on interpreting automatic age recognition on CQAs (a.k.a. age screening) as a regression task. To this effect, it compares state-of-the-art graph-based neural network regression and embedding models on a massive activity-graph encompassing ca. 16 and 837 million nodes (members) and edges, respectively. For this study, a large-scale subset of ca. 657,000 community fellows was automatically associated with their age via aligning their profile texts with a limited number of linguistic patterns. In short, our results show that Node2vec significantly outperforms other embeddings regardless of the regression model used for casting predictions. When this embedding is combined with Artificial Neural Network Regressions, we obtained our best configuration scoring a Root Mean Square Error (RMSE) of 8.39. An interesting qualitative feature of this embedding space is that age-based centroid vectors tend to form a trail ordered by age. Lastly, our outcomes also signal that activity graph based models can rival its counterparts based on image and textual inputs, paving the way for constructing effective multi-modal approaches. • We conducted age demographic analysis on a massive activity graph. • Several graph-based neural network regression models were compared. • Age-based centroid vectors tend to form a trail ordered by age. • Graph models can rival its counterparts based on image and textual inputs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.engappai.2023.106219 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Virtual communities Type: general – SubjectFull: Communities Type: general – SubjectFull: Online social networks Type: general – SubjectFull: Standard deviations Type: general Titles: – TitleFull: Neural age screening on question answering communities. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Timilsina, Mohan – PersonEntity: Name: NameFull: Figueroa, Alejandro IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2023:Part A Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09521976 Numbering: – Type: volume Value: 123 Titles: – TitleFull: Engineering Applications of Artificial Intelligence Type: main |
| ResultId | 1 |
