Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Kong, Quyu"'
In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal covariate information. DKMPP is an enhanced version of Deep Mixture Point Processes (DMPP), which us
Externí odkaz:
http://arxiv.org/abs/2310.05485
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via multi-output Gaussian processes (MOGP). A MOGP prior over the parameters
Externí odkaz:
http://arxiv.org/abs/2308.15364
Publikováno v:
Proceedings of the ACM Web Conference 2023 (WWW '23), May 1--5, 2023, Austin, TX, USA
Social media is being increasingly weaponized by state-backed actors to elicit reactions, push narratives and sway public opinion. These are known as Information Operations (IO). The covert nature of IO makes their detection difficult. This is furthe
Externí odkaz:
http://arxiv.org/abs/2211.14114
Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and can be overly focused and narro
Externí odkaz:
http://arxiv.org/abs/2109.00302
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of applications, their expressive ability is limited due to three key hyp
Externí odkaz:
http://arxiv.org/abs/2106.04844
Contact tracing has been extensively studied from different perspectives in recent years. However, there is no clear indication of why this intervention has proven effective in some epidemics (SARS) and mostly ineffective in some others (COVID-19). H
Externí odkaz:
http://arxiv.org/abs/2102.13349
Publikováno v:
In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM'21), pp. 918-921. New York, NY, USA: ACM. 2021
The impact of online social media on societal events and institutions is profound; and with the rapid increases in user uptake, we are just starting to understand its ramifications. Social scientists and practitioners who model online discourse as a
Externí odkaz:
http://arxiv.org/abs/2012.02370
Publikováno v:
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021
Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a disconnect between the practitioners of online social media analysis -- usua
Externí odkaz:
http://arxiv.org/abs/2006.06167
It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses
Externí odkaz:
http://arxiv.org/abs/2001.11132
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