Demo Data from SIGIR 2026 / reviewers in the wild / expert
Morgan Manella Greenfield
dblp:420/3640
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0008-2796-8982ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Deep learning architectures and training · 44% Time series and sequential data · 44% Knowledge representation and reasoning · 13% | |
| Databases, data mining, and information retrieval
2 papers |
Web and social media mining · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Web and social media mining
social media analysis |
1.3 | 2 | 2026 | SMART: A Social Movement Analysis & Reasoning Tool with Case Studies on #MeToo and #BlackLivesMatter · WWW 2026 DEEP: A Discourse Evolution Engine for Predictions About Social Movements · AAAI 2026 |
Machine learning › Time series and sequential data › time series analysis
time series forecasting |
1.0 | 1 | 2026 | DEEP: A Discourse Evolution Engine for Predictions About Social Movements · AAAI 2026 |
Machine learning › Deep learning architectures and training
transformer |
1.0 | 1 | 2026 | DEEP: A Discourse Evolution Engine for Predictions About Social Movements · AAAI 2026 |
Computational social science and digital humanities
social movement analysis |
1.0 | 1 | 2026 | DEEP: A Discourse Evolution Engine for Predictions About Social Movements · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
transformer · 3.0multivariate time series forecasting · 3.0social network analysis · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | DEEP: A Discourse Evolution Engine for Predictions About Social MovementsabstractNumerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025, which is publicly available for research purposes. Valerio La Gatta, Marco Postiglione, Jeremy Gilbert, Daniel W. Linna Jr., Morgan Manella Greenfield, Aaron D. Shaw, V. S. Subrahmanian |
AAAI | 5 |
| 2026 | SMART: A Social Movement Analysis & Reasoning Tool with Case Studies on #MeToo and #BlackLivesMatter
Valerio La Gatta, Marco Postiglione, Jeremy Gilbert, Daniel W. Linna Jr., Morgan Manella Greenfield, Aaron D. Shaw, V. S. Subrahmanian |
WWW | 5 |