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Found 5 papers, 1 papers with code
Improving Contextual Congruence Across Modalities for Effective Multimodal Marketing using Knowledge-infused Learning
The prevalence of smart devices with the ability to capture moments in multiple modalities has enabled users to experience multimodal information online.
Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS).
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks.
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming.
Predictive Analysis on Twitter: Techniques and Applications
Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide.
Social and Information Networks
