94,847 research outputs found
Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game
Social media has become a major communication channel for communities
centered around video games. Consequently, social media offers a rich data
source to study online communities and the discussions evolving around games.
Towards this end, we explore a large-scale dataset consisting of over 1 million
tweets related to the online multiplayer shooter Destiny and spanning a time
period of about 14 months using unsupervised clustering and topic modelling.
Furthermore, we correlate Twitter activity of over 3,000 players with their
playtime. Our results contribute to the understanding of online player
communities by identifying distinct player groups with respect to their Twitter
characteristics, describing subgroups within the Destiny community, and
uncovering broad topics of community interest.Comment: Accepted at IEEE Conference on Games 201
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
A high degree of topical diversity is often considered to be an important
characteristic of interesting text documents. A recent proposal for measuring
topical diversity identifies three elements for assessing diversity: words,
topics, and documents as collections of words. Topic models play a central role
in this approach. Using standard topic models for measuring diversity of
documents is suboptimal due to generality and impurity. General topics only
include common information from a background corpus and are assigned to most of
the documents in the collection. Impure topics contain words that are not
related to the topic; impurity lowers the interpretability of topic models and
impure topics are likely to get assigned to documents erroneously. We propose a
hierarchical re-estimation approach for topic models to combat generality and
impurity; the proposed approach operates at three levels: words, topics, and
documents. Our re-estimation approach for measuring documents' topical
diversity outperforms the state of the art on PubMed dataset which is commonly
used for diversity experiments.Comment: Proceedings of the 39th European Conference on Information Retrieval
(ECIR2017
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