| Presentation | 2022-06-25 Estimating tweet directivity using linguistic features Hidenori Kiyomoto, Kongmeng Liew, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Social Computing / Natural Language Processing / Risk Communication / Twitter / Directivity |
| Paper # | DE2022-4 |
| Date of Issue | 2022-06-17 (DE) |
| Conference Information | |
| Committee | DE |
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| Conference Date | 2022/6/24(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Musashino University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Social Computing |
| Chair | Naofumi Yoshida(Komazawa Univ.) |
| Vice Chair | Akiyoshi Matono(AIST) / Yu Suzuki(Gifu Univ.) |
| Secretary | Akiyoshi Matono(Kanagawa Inst. of Tech.) / Yu Suzuki(Osaka Univ.) |
| Assistant | Ken Honda(Komazawa Univ.) / Hiroki Nomiya(Kyoto Inst. of Tech) |
| Paper Information | |
| Registration To | Technical Committee on Data Engineering |
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| Language | JPN |
| 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) | |
| Keyword(1) | Social Computing |
| Keyword(2) | Natural Language Processing |
| Keyword(3) | Risk Communication |
| Keyword(4) | |
| Keyword(5) | Directivity |
| 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 |
| 5th Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| Date | 2022-06-25 |
| Paper # | DE2022-4 |
| Volume (vol) | vol.122 |
| Number (no) | DE-88 |
| Page | pp.pp.19-24(DE), |
| #Pages | 6 |
| Date of Issue | 2022-06-17 (DE) |