How Emotional and Contextual Annotations Involve in Sensemaking Processes of Foreign Language Social Media Posts

Lim, Hajin; Cosley, Dan; Fussell, Susan R.

doi:10.1145/3359171

Cited by 5 publications

(2 citation statements)

References 34 publications

(21 reference statements)

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“…Among the areas in which technological tools can contribute is monitoring pragmatic forces in messages such as sentiment [59], politeness [6,58] and formality [83] using text mining models. Additionally, crowdsourcing aids for figuring out other people's intentions and writing appropriate responses (e.g., [12]) could also be a viable option.…”

Section: Providing Both Language and Social Communication Helpmentioning

confidence: 99%

So, I Can Feel Normal: Participatory Design for Accessible Social Media Sites for Individuals with Traumatic Brain Injury

Lim

Kakonge

Yaxin

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Traumatic brain injury (TBI) can result in chronic sensorimotor, cognitive, psychosocial, and communication challenges that can limit social participation. Social media can be a useful outlet for social participation for individuals with TBI, but there are barriers to access. While research has drawn attention to the nature of access barriers, few studies have investigated technological solutions to address these barriers, particularly considering the perspectives of individuals with TBI. To address this gap in knowledge, we used a participatory approach to engage 10 adults with TBI in conceptualizing tools to address their challenges accessing Facebook. Participants described multifaceted challenges in using social media, including interface overload, social comparisons, and anxiety over self-presentation and communication after injury. They discussed their needs and preferences and generated ideas for design solutions. Our work contributes to designing assistive and accessibility technology to facilitate an equal access to the benefits of social media for individuals with TBI.CCS CONCEPTS • Human-centered computing → • Accessibility → • Accessibility design and evaluation methods

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Section: Providing Both Language and Social Communication Helpmentioning

confidence: 99%

So, I Can Feel Normal: Participatory Design for Accessible Social Media Sites for Individuals with Traumatic Brain Injury

Lim

Kakonge

Yaxin

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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show abstract

“…They tend to select the response with the greatest likelihood, i.e., a consensus response represented in training data [24], while users' personal preferences in linguistic style are ignored. As suggested by previous research [27,28], people often cannot comprehend machine translation results in part due to the difficulty of sense making, and the future design of machine translation systems should forage more useful information to enhance sense making. Linguistic style is one of the key components of natural languages, and it can significantly affect people's sense making of languages [21].…”

Section: Introductionmentioning

confidence: 97%

Building User-oriented Personalized Machine Translator based on User-Generated Textual Content

Zhang

Guan

Liu

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-the-art deep learning techniques like Transformer and pre-training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results.

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User-Controlled Content Translation in Social Media

Gupta

2021

26th International Conference on Intelligent User Interfaces

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How Emotional and Contextual Annotations Involve in Sensemaking Processes of Foreign Language Social Media Posts

Cited by 5 publications

References 34 publications

So, I Can Feel Normal: Participatory Design for Accessible Social Media Sites for Individuals with Traumatic Brain Injury

So, I Can Feel Normal: Participatory Design for Accessible Social Media Sites for Individuals with Traumatic Brain Injury

Building User-oriented Personalized Machine Translator based on User-Generated Textual Content

User-Controlled Content Translation in Social Media

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