Paper Abstract and Keywords |
Presentation |
2022-06-25 11:20
Estimating tweet directivity using linguistic features Hidenori Kiyomoto, Kongmeng Liew, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki (NAIST) DE2022-4 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
With the Covid-19 pandemic, governments and local authorities are often required to provide accurate and prompt information using social media. For the communication of such information, it is important for social media users themselves to know if they fall within the targeted demographics of these communications (age, gender, etc.), which we label here as ‘directivity’. Previous studies have mainly focused on examining the attributes of the information provider, but to our knowl- edge, there have not been any studies that examine the attributes of targeted users (receivers). In this study, we first assumed that tweets by magazine publishers are crafted for their targeted readership. We then collected tweets from the official Twitter accounts of these magazines and manually labeled the target age and gender of each magazine to create a dataset of tweet directivity. Using this dataset, we then classified the target user demographic (age group and gender) of these tweets through machine learning. We analyzed the results of this experiment and discussed the usefulness of our quantitative estimates of directivity. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Social Computing / Natural Language Processing / Risk Communication / Twitter / Directivity / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 88, DE2022-4, pp. 19-24, June 2022. |
Paper # |
DE2022-4 |
Date of Issue |
2022-06-17 (DE) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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DE2022-4 |
Conference Information |
Committee |
DE |
Conference Date |
2022-06-24 - 2022-06-25 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Musashino University |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Social Computing |
Paper Information |
Registration To |
DE |
Conference Code |
2022-06-DE |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Estimating tweet directivity using linguistic features |
Sub Title (in English) |
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Keyword(1) |
Social Computing |
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Natural Language Processing |
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Risk Communication |
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Twitter |
Keyword(5) |
Directivity |
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1st Author's Name |
Hidenori Kiyomoto |
1st Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
2nd Author's Name |
Kongmeng Liew |
2nd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
3rd Author's Name |
Shuntaro Yada |
3rd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
4th Author's Name |
Shoko Wakamiya |
4th Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
5th Author's Name |
Eiji Aramaki |
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Nara Institute of Science and Technology (NAIST) |
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Speaker |
Author-1 |
Date Time |
2022-06-25 11:20:00 |
Presentation Time |
15 minutes |
Registration for |
DE |
Paper # |
DE2022-4 |
Volume (vol) |
vol.122 |
Number (no) |
no.88 |
Page |
pp.19-24 |
#Pages |
6 |
Date of Issue |
2022-06-17 (DE) |
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