Sharath Chandra Guntuku

Also published as: Sharath Chandra Guntuku


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Understanding Social Support Expressed in a COVID-19 Online ForumCOVID-19 Online Forum
Anietie Andy | Brian Chu | Ramie Fathy | Barrington Bennett | Daniel Stokes | Sharath Chandra Guntuku
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis

In online forums focused on health and wellbeing, individuals tend to seek and give the following social support : emotional and informational support. Understanding the expressions of these social supports in an online COVID- 19 forum is important for : (a) the forum and its members to provide the right type of support to individuals and (b) determining the long term effects of the COVID-19 pandemic on the well-being of the public, thereby informing interventions. In this work, we build four machine learning models to measure the extent of the following social supports expressed in each post in a COVID-19 online forum : (a) emotional support given (b) emotional support sought (c) informational support given, and (d) informational support sought. Using these models, we aim to : (i) determine if there is a correlation between the different social supports expressed in posts e.g. when members of the forum give emotional support in posts, do they also tend to give or seek informational support in the same post? (ii) determine how these social supports sought and given changes over time in published posts. We find that (i) there is a positive correlation between the informational support given in posts and the emotional support given and emotional support sought, respectively, in these posts and (ii) over time, users tended to seek more emotional support and give less emotional support.


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Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic ModelingCOVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling
Mohammadzaman Zamani | H. Andrew Schwartz | Johannes Eichstaedt | Sharath Chandra Guntuku | Adithya Virinchipuram Ganesan | Sean Clouston | Salvatore Giorgi
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.


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Suicide Risk Assessment with Multi-level Dual-Context Language and BERTBERT
Matthew Matero | Akash Idnani | Youngseo Son | Salvatore Giorgi | Huy Vu | Mohammad Zamani | Parth Limbachiya | Sharath Chandra Guntuku | H. Andrew Schwartz
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with any-risk, personality factors from the non-suicide contexts provide distinction of the levels of risk : low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.


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Controlling Human Perception of Basic User Traits
Daniel Preoţiuc-Pietro | Sharath Chandra Guntuku | Lyle Ungar
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Much of our online communication is text-mediated and, lately, more common with automated agents. Unlike interacting with humans, these agents currently do not tailor their language to the type of person they are communicating to. In this pilot study, we measure the extent to which human perception of basic user trait information gender and age is controllable through text. Using automatic models of gender and age prediction, we estimate which tweets posted by a user are more likely to mis-characterize his traits. We perform multiple controlled crowdsourcing experiments in which we show that we can reduce the human prediction accuracy of gender to almost random an over 20 % drop in accuracy. Our experiments show that it is practically feasible for multiple applications such as text generation, text summarization or machine translation to be tailored to specific traits and perceived as such.