publications

2021

1. Topics of Nicotine-Related Discussions on Twitter: Infoveillance Study

   Jon-Patrick Allem, Allison Dormanesh, Anuja Majmundar, and 6 more authors

   J Med Internet Res Jun 2021

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   Background: Cultural trends in the United States, the nicotine consumer marketplace, and tobacco policies are changing. Objective: The goal of this study was to identify and describe nicotine-related topics of conversation authored by the public and social bots on Twitter, including any misinformation or misconceptions that health education campaigns could potentially correct. Methods: Twitter posts containing the term “nicotine” were obtained from September 30, 2018 to October 1, 2019. Methods were used to distinguish between posts from social bots and nonbots. Text classifiers were used to identify topics in posts (n=300,360). Results: Prevalent topics of posts included vaping, smoking, addiction, withdrawal, nicotine health risks, and quit nicotine, with mentions of going “cold turkey” and needing help in quitting. Cessation was a common topic, with mentions of quitting and stopping smoking. Social bots discussed unsubstantiated health claims including how hypnotherapy, acupuncture, magnets worn on the ears, and time spent in the sauna can help in smoking cessation. Conclusions: Health education efforts are needed to correct unsubstantiated health claims on Twitter and ultimately direct individuals who want to quit smoking to evidence-based cessation strategies. Future interventions could be designed to follow these topics of discussions on Twitter and engage with members of the public about evidence-based cessation methods in near real time when people are contemplating cessation.

2020

1. Punchline Detection Using Context-Aware Hierarchical Multimodal Fusion

   Akshat Choube, and Mohammad Soleymani

   In Proceedings of the 2020 International Conference on Multimodal Interaction Jun 2020

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   Humor has a history as old as humanity. Humor often induces laughter and elicits amusement and engagement. Humorous behavior involves behavior manifested in different modalities including language, voice tone, and gestures. Thus, automatic understanding of humorous behavior requires multimodal behavior analysis. Humor detection is a well-established problem in Natural Language Processing but its multimodal analysis is less explored. In this paper, we present a context-aware hierarchical fusion network for multimodal punchline detection. The proposed neural architecture first fuses the modalities two by two and then fuses all three modalities. The network also models the context of the punchline using Gated Recurrent Unit(s). The model’s performance is evaluated on UR-FUNNY database yielding state-of-the-art performance.

   @inproceedings{10.1145/3382507.3418891,
     author = {Choube, Akshat and Soleymani, Mohammad},
     title = {Punchline Detection Using Context-Aware Hierarchical Multimodal Fusion},
     year = {2020},
     isbn = {9781450375818},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3382507.3418891},
     doi = {10.1145/3382507.3418891},
     booktitle = {Proceedings of the 2020 International Conference on Multimodal Interaction},
     pages = {675–679},
     numpages = {5},
     keywords = {gated recurrent units, multimodal machine learning, neural networks, multimodal fusion},
     location = {Virtual Event, Netherlands},
     series = {ICMI '20}
   }

2018

1. Energy-Delay-Distortion Problem

   Rahul Vaze, Shreyas Chaudhari, Akshat Choube, and 1 more author

   In 2018 Twenty Fourth National Conference on Communications (NCC) Feb 2018

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   An energy-limited source trying to transmit multiple packets to a destination with possibly different sizes is considered. With limited energy, the source cannot potentially transmit all bits of all packets. In addition, there is a delay cost associated with each packet. Thus, the source has to choose, how many bits to transmit for each packet, and the order in which to transmit these bits, to minimize the cost of distortion (introduced by transmitting lower number of bits) and queueing plus transmission delay, across all packets. Assuming an exponential metric for distortion loss and linear delay cost, we show that the optimal order of transmission is the increasing order of packet sizes and optimization problem is jointly convex. Hence, the problem can be exactly solved using convex solvers, however, because of the complicated expression derived from the KKT conditions, no closed form solution can be found even with the simplest cost function choice made in the paper. To facilitate a more structured solution, a discretized version of the problem is also considered, where time and energy are divided in discrete amounts. In any time slot (fixed length), bits belonging to any one packet can be transmitted, while any discrete number of energy quanta can be used in any slot corresponding to any one packet, such that the total energy constraint is satisfied. The discretized problem is a special case of a multi-partitioning problem, where each packet’s utility is super-modular and the proposed greedy solution is shown to incur cost that is at most 2-times of the optimal cost.