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【論文紹介】Language in Our Time An Empirical Analysis of Hashtags / introduce-language-in-our-time-an-empirical-analysis-of-hashtags

【論文紹介】Language in Our Time An Empirical Analysis of Hashtags / introduce-language-in-our-time-an-empirical-analysis-of-hashtags

2019年5月24日 WEBエンジニア勉強会 #13 (https://web-engineer-meetup.connpass.com/event/128855/)での発表資料です

WWW2019 の 『Language in Our Time: An Empirical Analysis of Hashtags』 (https://arxiv.org/abs/1905.04590) という論文についてさらっと紹介させていただきました。👀

Yuya Matsumura

May 24, 2019
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  1. ʲ࿦จ঺հʳLanguage in Our Time: An Empirical Analysis of Hashtags -

    Instagram ʹ͓͚Δ #ϋογϡλά ͷ෼ੳ- WEBΤϯδχΞษڧձ #13 Yuya Matsumura (@yu-ya4) 24 May 2019
  2. ✓ Yuya Matsumura ✓ Software Engineer ✓ Wantedly, Inc. Recommendation

    Team ✓ Interested in Information Retrieval, Machine Learning Self-Introduction @yu-ya4 @yu__ya4
  3. ✓ Webٕज़શൠʹؔ͢Δ࠷΋ݖҖͷ͋Δࠃࡍձٞ ✓ Google, Baidu, Amazon, facebook…. ͳͲͷ໊ͩͨΔεϙϯαʔ ✓ ࠾୒཰͸

    WWW 2018 Ͱ 14.8% (171/1155) ✓ 2019 ೥͸ 5/13 - 5/17 ʹαϯϑϥϯγείͰ։࠵͞Εͨ ✓ ݕࡧ΍ηΩϡϦςΟɼػցֶश͔Βࣾձ໰୊·Ͱ༷ʑͳτϐοΫ The Web Conference (WWW) 5IF8FC$POGFSFODFIUUQTXXXUIFXFCDPOGPSH
  4. ✓ Who Watches the Watchmen: Exploring Complaints on the Web

    ✓ The Music Streaming Sessions Dataset ✓ Blockchain Mining Games with Pay-Forward ✓ GhostLink: Mining Latent Influence Networks for Influence-aware Item Recommendation ✓ Language in Our Time: An Empirical Analysis of Hashtags Examples of sessions IUUQTXXXUIFXFCDPOGPSHBDDFQUFEQBQFST
  5. ✓ Who Watches the Watchmen: Exploring Complaints on the Web

    ✓ The Music Streaming Sessions Dataset ✓ Blockchain Mining Games with Pay-Forward ✓ GhostLink: Mining Latent Influence Networks for Influence-aware Item Recommendation ✓ Language in Our Time: An Empirical Analysis of Hashtags Examples of sessions IUUQTXXXUIFXFCDPOGPSHBDDFQUFEQBQFST ˠ͜ͷ࿦จͷ಺༰ʹ͍ͭͯ؆୯ʹ͝঺հ
  6. Language in Our Time: An Empirical Analysis of Hashtags IUUQTBSYJWPSHBCT

    "VUIPST:BOH;IBOH 1SPDFFEJOH 8885IF8PSME8JEF8FC$POGFSFODF Pages 2378-2389
  7. ✓ 2010೥ͷऴΘΓ͔Β2015೥ͷऴΘΓ·Ͱͷ໿5೥෼ͷσʔλΛऩू ✓ New York, Los Angeles, London ʹҐஔ৘ใ͖ͭͰ౤ߘͨ͜͠ͱͷ͋Δ 51,527

    usersʢ౤ߘ਺ͳͲͰ଍੾Γ͋Γʣ ✓ more than 39 million posts, more than 7 million hashtags Instagram ͷେن໛σʔλɾηοτ
  8. ౤ߘճ਺ TOP10 ͷϋογϡλά ✓ #nyc ΍ #london ͳͲͷ஍Ҭಛ༗ͷ΋ͷ΋ଟ͘ݟΒΕΔ ✓ ҰํͰɼ#love

    ͱ͔ #artɼ#travel Έ͍ͨͳ general ͳϋογϡλά΋ ͨ͘͞ΜݟΒΕΔͷͰσʔληοτͱͯ͠໰୊ͳͦ͞͏
  9. 1. What are the temporal and spatial patterns of hashtags?ʢ࣌ؒ΍৔ॴͱϋογϡλάͷؔ܎ʣ

    2. Do hashtags exhibit semantic displacement? ʢϋογϡλάͷҙຯͷมԽʣ 3. Can hashtags be used to infer social relations?ʢϋογϡλάΛ༻͍ͨιʔγϟϧͳ༑ୡؔ܎ͷਪఆʣ 3 ͭͷ؍఺ʢResearch Questionsʣ
  10. 1. What are the temporal and spatial patterns of hashtags?ʢ࣌ؒ΍৔ॴͱϋογϡλάͷؔ܎ʣ

    2. Do hashtags exhibit semantic displacement? ʢϋογϡλάͷҙຯͷมԽʣ 3. Can hashtags be used to infer social relations?ʢϋογϡλάΛ༻͍ͨιʔγϟϧͳ༑ୡؔ܎ͷਪఆʣ 3 ͭͷ؍఺ʢResearch Questionsʣ
  11. 1. What are the temporal and spatial patterns of hashtags?ʢ࣌ؒ΍৔ॴͱϋογϡλάͷؔ܎ʣ

    2. Do hashtags exhibit semantic displacement? ʢϋογϡλάͷҙຯͷมԽʣ 3. Can hashtags be used to infer social relations?ʢϋογϡλάΛ༻͍ͨιʔγϟϧͳ༑ୡؔ܎ͷਪఆʣ 3 ͭͷ؍఺ʢResearch Questionsʣ
  12. 1. What are the temporal and spatial patterns of hashtags?ʢ࣌ؒ΍৔ॴͱϋογϡλάͷؔ܎ʣ

    2. Do hashtags exhibit semantic displacement? ʢϋογϡλάͷҙຯͷมԽʣ 3. Can hashtags be used to infer social relations?ʢϋογϡλάΛ༻͍ͨιʔγϟϧͳ༑ୡؔ܎ͷਪఆʣ 3 ͭͷ؍఺ʢResearch Questionsʣ
  13. ✓ ༑ୡؔ܎ʹ͋Ε͹ಉ͡Α͏ͳϋογϡλάΛ౤ߘ͢ΔͷͰ͸ͳ͍͔ʁ ✓ ڞ௨ͷϋογϡλάͷ਺͕ଟ͚Ε͹༑ୡʁ → ͚ͬ͜͏গͳ͍͔Βݫ͍͠ ✓ User ͱ Hashtag

    ͷؔ܎ΛάϥϑͰද͢ ✓ Random Walk తͳΞϓϩʔνΛ༻͍ͯɼUser ΛϕΫτϧͰදݱʢྲྀߦΓ ͷ Graph Embeddingʣͯ͠ྨࣅ౓Ͱਪఆʂ ϋογϡλάΛ༻͍ͨιʔγϟϧͳ༑ୡؔ܎ͷਪఆ
  14. ✓ The Web Conference ͍ͬͯ͏ Web ܥͷΧϯϑΝϨϯε͕͋ΔΑʂ ✓ ঺հͨ͠Α͏ͳׂͱಡΈ΍͍͢࿦จ΋౤ߘ͞ΕͯΔΑʂ ✓

    ීஈͷۀ຿ʹ׆͔ͤΔ͔΋ʂʁ ✓ Έͳ͞Μ΋ੋඇڵຯΛ͍͚࣋ͬͯͨͩΕ͹ʂ ఻͔͑ͨͬͨ͜ͱ