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SDAI 2019: EAI International Conference on Social Data and Artificial Intelligence

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截稿日期:
2019-03-15
通知日期:
2019-05-15
会议日期:
2019-08-07
会议地点:
Minneapolis, Minnesota, USA
浏览: 14249   关注: 0   参加: 0

会伴指数 (CP-I)

42.1 / 100
全站第 4,137 名 / 共 5,682 个会议 · 前 73%

人工智能与机器学习 第 536 / 739

证据有限：这个会议不在 CCF / ICORE / QUALIS 任何一份榜单里，也没有录用率数据，因此分数的大部分回落到了中性基准。
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%) 无数据 —— 按中性基准 50 分计入 —
社区关注 (10%)
8
资料公开度 (15%)
25

用到的输入: 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 25% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-18

征稿

SDAI 2019 (EAI International Conference on Social Data and Artificial Intelligence) is an academic conference held in Minneapolis, Minnesota, USA on 2019-08-07. The paper submission deadline is 2019-03-15. Acceptance notifications are sent on 2019-05-15.

Scope There are two main themes of the conference A. Social Data and Artificial Intelligence: One of the themes of this conference is to analyze and formulate new perspective of social theories and develop computational algorithms using large scale data sets. The research articles should incorporate different computational algorithms and data analysis techniques to address the problem of inconsistencies, inaccuracies and inadequacies of social data for policy making. The use of advanced artificial intelligence algorithms and technologies is encouraged. B. Social Media Data Analysis in Health: Research suggests that more and more people rely on online sources for health information including symptoms, treatments and general health-related advice. Moreover, the user behaviour currently active on social media demonstrates an openness to share facts related to their current health status. Such data could be used to provide real-time tracking and prediction of the spread of disease and other health concerns, or provide vital information about the effectiveness of the public health awareness strategies of health agencies such as Health Canada, the Centre for Disease Control (CDC) or the World Health Organization (WHO). However, the current understanding of online health data produced through social networks is limited in important ways: (a) existing databases are project specific and data gathering mechanisms are time-constrained; (b) existing health-tracking tools depend on single interfaces such as Facebook, Twitter or Instagram, and (c) there is a lack of capacity for real-time mapping of health issues. Topics Authors are encouraged to submit previously unpublished original papers in the following areas: Social Data inadequacies and inconsistencies Predictive models of social behaviours Infrastructure and architecture for testing social theories Data collection and analysis platforms Relevance of IoT for social science theories Building capacity to continuously collect data across a range of social media networks Real-time tracking and filtering of noisy social media datasets Designing decision-making tools to empower organizations in harnessing social media and AI for their campaigns Cross-validating the predictive models of social media datasets with ground truth data Developing frameworks and algorithms to perform real-time analysis of social media datasets. Publication All registered papers will be submitted for publication by Springer and made available through SpringerLink Digital Library. Proceedings will be submitted for inclusion in leading indexing services, Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL). Authors of selected best accepted and presented papers will be invited to submit an extended version to Mobile Networks and Applications (MONET) Journal (IF: 2.497) All accepted authors are eligible to submit an extended version in a fast track of: EAI Endorsed Transactions on Security and Safety
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