| Title |
Temporal Ordinance Mining for Event-Driven Social Media Reaction Analytics |
| ID_Doc |
78928 |
| Authors |
Varde, AS; de Melo, G; Dong, BX |
| Title |
Temporal Ordinance Mining for Event-Driven Social Media Reaction Analytics |
| Year |
2023 |
| Published |
|
| DOI |
10.1145/3543873.3587674 |
| Abstract |
As a growing number of policies are adopted to address the substantial rise in urbanization, there is a significant push for smart governance, endowing transparency in decision-making and enabling greater public involvement. The thriving concept of smart governance goes beyond just cities, ultimately aiming at a smart planet. Ordinances (local laws) affect our life with regard to health, business, etc. This is particularly notable during major events such as the recent pandemic, which may lead to rapid changes in ordinances, pertaining for instance to public safety, disaster management, and recovery phases. However, many citizens view ordinances as impervious and complex. This position paper proposes a research agenda enabling novel forms of ordinance content analysis over time and temporal web question answering (QA) for both legislators and the broader public. Along with this, we aim to analyze social media posts so as to track the public opinion before and after the introduction of ordinances. Challenges include addressing concepts changing over time and infusing subtle human reasoning in mining, which we aim to address by harnessing terminology evolution methods and commonsense knowledge sources, respectively. We aim to make the results of the historical ordinance mining and event-driven analysis seamlessly accessible, relying on a robust semantic understanding framework to flexibly support web QA. |
| Author Keywords |
Commonsense knowledge; historical data; local laws; machine learning; NLP; social media; smart governance; urban policy; terminology evolution; text mining; web Q&A |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:001124276300227 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
| PDF |
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