• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

Twitter 情感曲线模型。

A model for the Twitter sentiment curve.

机构信息

ADAMSS Center, Università degli Studi di Milano, Milan, Italy.

IMT School for Advanced Studies, Lucca, Italy.

出版信息

PLoS One. 2021 Apr 15;16(4):e0249634. doi: 10.1371/journal.pone.0249634. eCollection 2021.

DOI:10.1371/journal.pone.0249634
PMID:33857207
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8049311/
Abstract

Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the Pólya urn, introduced and studied in Aletti and Crimaldi (2019, 2020), which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard Pólya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.

摘要

推特是最常用于政治传播的在线平台之一，因为它的信息简洁（特别适合政治口号），并且信息传播迅速。尤其是当论点激发了用户的情绪时，推特上的内容会以极快的速度被分享，因此研究推文的情绪对于预测讨论的发展和相关叙事的记录至关重要。在本文中，我们提出了一个能够再现与特定主题和时期相关的推文情绪动态的模型，并能够根据观察到的过去来预测未来帖子的情绪。该模型是 Aletti 和 Crimaldi（2019，2020）提出并研究的 Pólya urn 的一个最新变体，其特点是“局部”增强，即主要基于最近观察结果的增强机制，以及预测均值的随机持续波动。特别是，后一个特征能够捕捉到情绪曲线的趋势波动。虽然所提出的模型非常通用，也可以用于其他情境，但它已经在几个推特数据集上进行了测试，并显示出比标准的 Pólya urn 模型更好的性能。此外，不同数据集上的不同性能突出了对公共事件的不同情绪敏感性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/ecc643e8608b/pone.0249634.g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/707a5fdc31f9/pone.0249634.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/048b8122239a/pone.0249634.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/155dedc38128/pone.0249634.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/fb6ce5744c54/pone.0249634.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/f88659b9378e/pone.0249634.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/e205002b0355/pone.0249634.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/d6f723273b31/pone.0249634.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/d0714a0f62f9/pone.0249634.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/adc5f0012672/pone.0249634.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/373c1a2ad52d/pone.0249634.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/846276d94dfa/pone.0249634.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/eac7a3f8b89d/pone.0249634.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/1c52c266a5ab/pone.0249634.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/d9ee9c32c206/pone.0249634.g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/5fc36d4fb678/pone.0249634.g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/dfb4942ef242/pone.0249634.g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/76a270bebac4/pone.0249634.g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/4beb9fdaa69f/pone.0249634.g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/2f3a117f0a60/pone.0249634.g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/4cfda059ad57/pone.0249634.g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/405f7aded75b/pone.0249634.g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/16fe88ed5b15/pone.0249634.g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/1553ff204c3d/pone.0249634.g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/ecc643e8608b/pone.0249634.g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/707a5fdc31f9/pone.0249634.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/048b8122239a/pone.0249634.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/155dedc38128/pone.0249634.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/fb6ce5744c54/pone.0249634.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/f88659b9378e/pone.0249634.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/e205002b0355/pone.0249634.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/d6f723273b31/pone.0249634.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/d0714a0f62f9/pone.0249634.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/adc5f0012672/pone.0249634.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/373c1a2ad52d/pone.0249634.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/846276d94dfa/pone.0249634.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/eac7a3f8b89d/pone.0249634.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/1c52c266a5ab/pone.0249634.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/d9ee9c32c206/pone.0249634.g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/5fc36d4fb678/pone.0249634.g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/dfb4942ef242/pone.0249634.g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/76a270bebac4/pone.0249634.g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/4beb9fdaa69f/pone.0249634.g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/2f3a117f0a60/pone.0249634.g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/4cfda059ad57/pone.0249634.g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/405f7aded75b/pone.0249634.g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/16fe88ed5b15/pone.0249634.g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/1553ff204c3d/pone.0249634.g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/8049311/ecc643e8608b/pone.0249634.g024.jpg

相似文献

1
A model for the Twitter sentiment curve.Twitter 情感曲线模型。
PLoS One. 2021 Apr 15;16(4):e0249634. doi: 10.1371/journal.pone.0249634. eCollection 2021.
2
Identifying Sentiment of Hookah-Related Posts on Twitter.识别推特上与水烟相关帖子的情感倾向。
JMIR Public Health Surveill. 2017 Oct 18;3(4):e74. doi: 10.2196/publichealth.8133.
3
Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study.推特上关于大麻的讨论存在地域差异：一项信息流行病学研究。
JMIR Public Health Surveill. 2020 Oct 5;6(4):e18540. doi: 10.2196/18540.
4
Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts.南非城市民众对 COVID-19 疫苗的看法：对 Twitter 帖子的分析。
Front Public Health. 2022 Aug 12;10:987376. doi: 10.3389/fpubh.2022.987376. eCollection 2022.
5
Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use-Associated Lung Injury Outbreak: Comparative Study.主题和情绪围绕着 2019 年电子烟和蒸气相关肺损伤爆发期间 Twitter 和 Reddit 上的蒸气：比较研究。
J Med Internet Res. 2022 Dec 13;24(12):e39460. doi: 10.2196/39460.
6
Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study.利用推特研究美国基于网络的患者体验情绪：纵向研究。
J Med Internet Res. 2018 Oct 12;20(10):e10043. doi: 10.2196/10043.
7
Mpox Panic, Infodemic, and Stigmatization of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis of a Large, Multilingual Social Media Database.猴痘恐慌、信息疫情以及对双灵、女同性恋、男同性恋、双性恋、跨性别、酷儿或疑问、间性、无性社群的污名化：大规模多语言社交媒体数据库的地理空间分析、主题建模和情感分析。
J Med Internet Res. 2023 May 1;25:e45108. doi: 10.2196/45108.
8
Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter.应用多种数据收集工具量化推特上的人乳头瘤病毒疫苗传播情况
J Med Internet Res. 2016 Dec 5;18(12):e318. doi: 10.2196/jmir.6670.
9
Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study.在 Twitter 上识别炎症性肠病患者并从他们的个人经验中学习：回顾性队列研究。
J Med Internet Res. 2022 Aug 2;24(8):e29186. doi: 10.2196/29186.
10
Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study.关于新冠疫情的推文主题、趋势和情绪：时间信息监测研究
J Med Internet Res. 2020 Oct 23;22(10):e22624. doi: 10.2196/22624.

引用本文的文献

1
Fluctuations and extreme events in the public attention on Italian legislative elections.意大利立法选举中公众关注度的波动和极端事件。
Sci Rep. 2024 Oct 1;14(1):22804. doi: 10.1038/s41598-024-69354-y.
2
From an election to an insurrection: Investigating differential engagement and sentiment in the #defundthepolice and #defendthepolice network on Twitter.从选举到暴动：在 Twitter 上调查#defundthepolice 和#defendthepolice 网络中的不同参与度和情绪。
PLoS One. 2024 Mar 21;19(3):e0289041. doi: 10.1371/journal.pone.0289041. eCollection 2024.
3
Interacting innovation processes.

本文引用的文献

1
Flow of online misinformation during the peak of the COVID-19 pandemic in Italy.意大利新冠疫情高峰期的在线错误信息传播情况。
EPJ Data Sci. 2021;10(1):34. doi: 10.1140/epjds/s13688-021-00289-4. Epub 2021 Jul 6.
2
The spread of low-credibility content by social bots.社交机器人传播低可信度内容。
Nat Commun. 2018 Nov 20;9(1):4787. doi: 10.1038/s41467-018-06930-7.
3
Bots increase exposure to negative and inflammatory content in online social systems.机器人增加了在线社交系统中负面和煽动性内容的曝光率。
交互创新过程。
Sci Rep. 2023 Oct 11;13(1):17187. doi: 10.1038/s41598-023-43967-1.
Proc Natl Acad Sci U S A. 2018 Dec 4;115(49):12435-12440. doi: 10.1073/pnas.1803470115. Epub 2018 Nov 20.
4
Echo Chambers: Emotional Contagion and Group Polarization on Facebook.回音室效应：脸书上的情绪传染与群体极化。
Sci Rep. 2016 Dec 1;6:37825. doi: 10.1038/srep37825.
5
Emotional Dynamics in the Age of Misinformation.虚假信息时代的情绪动态
PLoS One. 2015 Sep 30;10(9):e0138740. doi: 10.1371/journal.pone.0138740. eCollection 2015.
6
Weighted networks as randomly reinforced urn processes.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Feb;87(2):020106. doi: 10.1103/PhysRevE.87.020106. Epub 2013 Feb 28.
7
Stochastic dynamical model of a growing citation network based on a self-exciting point process.基于自激发点过程的引文网络增长的随机动力模型。
Phys Rev Lett. 2012 Aug 31;109(9):098701. doi: 10.1103/PhysRevLett.109.098701. Epub 2012 Aug 28.
8
Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures.在不同的文化中，昼夜节律和季节性情绪会随工作、睡眠和日照时间的变化而变化。
Science. 2011 Sep 30;333(6051):1878-81. doi: 10.1126/science.1202775.
9
Collective emotions online and their influence on community life.网络集体情绪及其对社区生活的影响。
PLoS One. 2011;6(7):e22207. doi: 10.1371/journal.pone.0022207. Epub 2011 Jul 27.