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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.02064">arXiv:2302.02064</a> <span> [<a href="https://arxiv.org/pdf/2302.02064">pdf</a>, <a href="https://arxiv.org/format/2302.02064">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Lived Experience Matters: Automatic Detection of Stigma on Social Media Toward People Who Use Substances </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Giorgi%2C+S">Salvatore Giorgi</a>, <a href="/search/cs?searchtype=author&query=Bellew%2C+D">Douglas Bellew</a>, <a href="/search/cs?searchtype=author&query=Habib%2C+D+R+S">Daniel Roy Sadek Habib</a>, <a href="/search/cs?searchtype=author&query=Sherman%2C+G">Garrick Sherman</a>, <a href="/search/cs?searchtype=author&query=Sedoc%2C+J">Joao Sedoc</a>, <a href="/search/cs?searchtype=author&query=Smitterberg%2C+C">Chase Smitterberg</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Amanda Devoto</a>, <a href="/search/cs?searchtype=author&query=Himelein-Wachowiak%2C+M">McKenzie Himelein-Wachowiak</a>, <a href="/search/cs?searchtype=author&query=Curtis%2C+B">Brenda Curtis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.02064v2-abstract-short" style="display: inline;"> Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02064v2-abstract-full').style.display = 'inline'; document.getElementById('2302.02064v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.02064v2-abstract-full" style="display: none;"> Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people who use substances have not. This paper explores stigma toward PWUS using a data set of roughly 5,000 public Reddit posts. We performed a crowd-sourced annotation task where workers are asked to annotate each post for the presence of stigma toward PWUS and answer a series of questions related to their experiences with substance use. Results show that workers who use substances or know someone with a substance use disorder are more likely to rate a post as stigmatizing. Building on this, we use a supervised machine learning framework that centers workers with lived substance use experience to label each Reddit post as stigmatizing. Modeling person-level demographics in addition to comment-level language results in a classification accuracy (as measured by AUC) of 0.69 -- a 17% increase over modeling language alone. Finally, we explore the linguist cues which distinguish stigmatizing content: PWUS substances and those who don't agree that language around othering ("people", "they") and terms like "addict" are stigmatizing, while PWUS (as opposed to those who do not) find discussions around specific substances more stigmatizing. Our findings offer insights into the nature of perceived stigma in substance use. Additionally, these results further establish the subjective nature of such machine learning tasks, highlighting the need for understanding their social contexts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02064v2-abstract-full').style.display = 'none'; document.getElementById('2302.02064v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication the 2024 International AAAI Conference on Web and Social Media (ICWSM)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.01802">arXiv:2202.01802</a> <span> [<a href="https://arxiv.org/pdf/2202.01802">pdf</a>, <a href="https://arxiv.org/format/2202.01802">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+T">Tingting Liu</a>, <a href="/search/cs?searchtype=author&query=Giorgi%2C+S">Salvatore Giorgi</a>, <a href="/search/cs?searchtype=author&query=Tao%2C+X">Xiangyu Tao</a>, <a href="/search/cs?searchtype=author&query=Guntuku%2C+S+C">Sharath Chandra Guntuku</a>, <a href="/search/cs?searchtype=author&query=Bellew%2C+D">Douglas Bellew</a>, <a href="/search/cs?searchtype=author&query=Curtis%2C+B">Brenda Curtis</a>, <a href="/search/cs?searchtype=author&query=Ungar%2C+L">Lyle Ungar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.01802v3-abstract-short" style="display: inline;"> Adaptive mobile device-based health interventions often use machine learning models trained on non-mobile device data, such as social media text, due to the difficulty and high expense of collecting large text message (SMS) data. Therefore, understanding the differences and generalization of models between these platforms is crucial for proper deployment. We examined the psycho-linguistic differen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01802v3-abstract-full').style.display = 'inline'; document.getElementById('2202.01802v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.01802v3-abstract-full" style="display: none;"> Adaptive mobile device-based health interventions often use machine learning models trained on non-mobile device data, such as social media text, due to the difficulty and high expense of collecting large text message (SMS) data. Therefore, understanding the differences and generalization of models between these platforms is crucial for proper deployment. We examined the psycho-linguistic differences between Facebook and text messages, and their impact on out-of-domain model performance, using a sample of 120 users who shared both. We found that users use Facebook for sharing experiences (e.g., leisure) and SMS for task-oriented and conversational purposes (e.g., plan confirmations), reflecting the differences in the affordances. To examine the downstream effects of these differences, we used pre-trained Facebook-based language models to estimate age, gender, depression, life satisfaction, and stress on both Facebook and SMS. We found no significant differences in correlations between the estimates and self-reports across 6 of 8 models. These results suggest using pre-trained Facebook language models to achieve better accuracy with just-in-time interventions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01802v3-abstract-full').style.display = 'none'; document.getElementById('2202.01802v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to the 17th International AAAI Conference on Web and Social Media (ICWSM), 2023</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" 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