Demo Data from SIGIR 2026 / reviewers in the wild / expert
Shounak Gore
dblp:178/2610
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1
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.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Web and social media mining · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Web and social media mining › user engagement
social media engagement |
0.2 | 1 | 2016 | Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms · KDD 2016 |
Algorithmic game theory and mechanism design
cooperative game theory |
0.2 | 1 | 2016 | Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms · KDD 2016 |
Algorithmic game theory and mechanism design › coalition formation
team formation |
0.1 | 1 | 2016 | Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms · KDD 2016 |
Methods — techniques the papers use, named apart from their topics
shapley value · 0.5game theory · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media PlatformsabstractThe challenges of assessing the "health" of online social media platforms and strategically growing them are recognized by many practitioners and researchers. For those platforms that primarily rely on user-generated content, the reach -- the degree of participation referring to the percentage and involvement of users -- is a key indicator of success. This paper lays a theoretical foundation for measuring engagement as a driver of reach that achieves growth via positive externality effects. The paper takes a game theoretic approach to quantifying engagement, viewing a platform's social capital as a cooperatively created value and finding a fair distribution of this value among the contributors. It introduces engagement capacity, a measure of the ability of users and user groups to engage peers, and formulates the Engaging Team Formation Problem (EngTFP) to identify the sets of users that "make a platform go". We show how engagement capacity can be useful in characterizing forum user behavior and in the reach maximization efforts. We also stress how engagement analysis differs from influence measurement. Computational investigations with Twitter and Health Forum data reveal the properties of engagement capacity and the utility of EngTFP. Alexander G. Nikolaev, Shounak Gore, Venu Govindaraju |
KDD | 2 |