Keyu Chen’s research while affiliated with Yale-New Haven Hospital and other places

We investigate how representations of Syrian refugees (2011-2021) differ across US partisan news outlets. We analyze 47,388 articles from the online US media about Syrian refugees to detail differences in reporting between left- and right-leaning media. We use various NLP techniques to understand these differences. Our polarization and question answering results indicated that left-leaning media tended to represent refugees as child victims, welcome in the US, and right-leaning media cast refugees as Islamic terrorists. We noted similar results with our sentiment and offensive speech scores over time, which detail possibly unfavorable representations of refugees in right-leaning media. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to intervene around refugee representations, and design communications campaigns that improve the way society sees refugees and possibly aid refugee outcomes.

The Russian disinformation campaign uses pro-Russia memes to polarize Americans, and increase support for the Russian invasion of Ukraine. Thus, it is critical for governments and similar stakeholders to identify pro-Russia memes, countering them with evidence-based information. Identifying broad meme themes is crucial for developing a targeted and strategic counter response. There are also a range of pro-Ukraine memes that bolster support for the Ukrainian cause. As such, we need to identify pro-Ukraine memes and aid with their dissemination to augment global support for Ukraine. We address the indicated issues through the following contributions: 1) Creation of an annotated dataset of pro-Russia (N = 70) and pro-Ukraine (N = 121) memes regarding the Ukraine conflict; 2) Identification of broad themes within the pro-Russia and pro-Ukraine meme categories. Broadly, our findings indicated that pro-Russia memes fall into thematic categories that seek to undermine specific elements of US and their allies’ policy and culture. Pro-Ukraine memes are far more diffuse thematically, highlighting admiration for Ukraine’s people and its leadership. Stakeholders may utilize our findings to develop targeted strategies to mitigate Russian influence operations - possibly reducing effects of the conflict.

In this paper, we investigate how vaping is framed differently (2008–2021) between US news and social media. We analyze 15,711 news articles and 1,231,379 Facebook posts about vaping to study the differences in framing between media varieties. We use word embeddings to provide two-dimensional visualizations of the semantic changes around vaping for news and for social media. We detail that news media framing of vaping shifted over time in line with emergent regulatory trends, such as; flavored vaping bans, with little discussion around vaping as a smoking cessation tool. We found that social media discussions were far more varied, with transitions toward vaping both as a public health harm and as a smoking cessation tool. Our cloze test, dynamic topic models, and question answering showed similar patterns, where social media, but not news media, characterizes vaping as combustible cigarette substitute. We use n-grams and LDA topic models to detail that social media data first centered on vaping as a smoking cessation tool, and in 2019 moved toward narratives around vaping regulation, similar to news media frames. Overall, social media tracks the evolution of vaping as a social practice, while news media reflects more risk based concerns. A strength of our work is how the different techniques we have applied validate each other.

In this paper, we provide a descriptive overview of how vaping is framed differently between various online plat- forms (Wikipedia, Quora, Medium, Reddit, Stack Exchange, wikiHow, Facebook, online news media). We provide an overview of >1M posts and news articles about vaping to study the differences in framing between online platforms. Findings indicate an inconsistent framing around vaping across platforms. Stakeholders may utilize our findings to inter- vene around the framing of vaping, and may design commu- nications campaigns that improve the way society sees vap- ing, possibly aiding smoking cessation; and reducing youth vaping.

The spread of information through social media platforms can create environments possibly hostile to vulnerable communities and silence certain groups in society. To mitigate such instances, several models have been developed to detect hate and offensive speech. Since detecting hate and offensive speech in social media platforms could incorrectly exclude individuals from social media platforms, which can reduce trust, there is a need to create explainable and interpretable models. Thus, we build an explainable and interpretable high performance model based on the XGBoost algorithm, trained on Twitter data. For unbalanced Twitter data, XGboost outperformed the LSTM, AutoGluon, and ULMFiT models on hate speech detection with an F1 score of 0.75 compared to 0.38 and 0.37, and 0.38 respectively. When we down-sampled the data to three separate classes of approximately 5,000 tweets, XGBoost performed better than LSTM, AutoGluon, and ULMFiT; with F1 scores for hate speech detection of 0.79 vs 0.69, 0.77, and 0.66 respectively. XGBoost also performed better than LSTM, AutoGluon, and ULMFiT in the down-sampled version for offensive speech detection with F1 score of 0.83 vs 0.88, 0.82, and 0.79 respectively. We use Shapley Additive Explanations (SHAP) on our XGBoost models’ outputs to makes it explainable and interpretable compared to LSTM, AutoGluon and ULMFiT that are black-box models. KeywordsTransparencyXGBoostPerformanceMachine learningNatural language processingHateOffensive

Studying how vaping is framed on various knowledge dissemination platforms (e.g., Quora, Reddit, Wikipedia) is central to understanding the process of knowledge dissemination around vaping. Such understanding can help us craft tools specific to each platform, to dispel vaping misperceptions and reinforce evidence-based information. We analyze 1,888 articles and 1,119,453 vaping posts to study how vaping is framed across multiple knowledge dissemination platforms (Wikipedia, Quora, Medium, Reddit, Stack Exchange, wikiHow). We use NLP techniques to understand these differences. As an example, regarding question answering results, for the question What is vaping for?, we note answers framing vaping as a smoking cessation tool in Quora, Medium, and Stack Exchange. Reddit tended to frame vaping as a hobby. Wikipedia had a mix of answers, some centered on EVALI, and others on vaping as harm reduction. Broadly, results indicate that Quora is an appropriate venue for those looking to transition from smoking to vaping. Other platforms (Reddit, wikiHow) are more for vaping hobbyists and may not sufficiently dissuade youth vaping. Conversely, Wikipedia may exaggerate vaping harms, dissuading smokers from transitioning. A strength of our work is how the different techniques we have applied validate each other. Stakeholders may utilize our findings to design vaping regulation that clarifies the role of vapes as a smoking cessation tool.

The spread of information through social media platforms can create environments possibly hostile to vulnerable communities and silence certain groups in society. To mitigate such instances, several models have been developed to detect hate and offensive speech. Since detecting hate and offensive speech in social media platforms could incorrectly exclude individuals from social media platforms, which can reduce trust, there is a need to create explainable and interpretable models. Thus, we build an explainable and interpretable high performance model based on the XGBoost algorithm, trained on Twitter data. For unbalanced Twitter data, XGboost outperformed the LSTM, AutoGluon, and ULMFiT models on hate speech detection with an F1 score of 0.75 compared to 0.38 and 0.37, and 0.38 respectively. When we down-sampled the data to three separate classes of approximately 5000 tweets, XGBoost performed better than LSTM, AutoGluon, and ULMFiT; with F1 scores for hate speech detection of 0.79 vs 0.69, 0.77, and 0.66 respectively. XGBoost also performed better than LSTM, AutoGluon, and ULMFiT in the down-sampled version for offensive speech detection with F1 score of 0.83 vs 0.88, 0.82, and 0.79 respectively. We use Shapley Additive Explanations (SHAP) on our XGBoost models' outputs to makes it explainable and interpretable compared to LSTM, AutoGluon and ULMFiT that are black-box models.

We analyze 1,888 articles and 1,119,453 vaping posts to study how vaping is framed across multiple knowledge dissemination platforms (Wikipedia, Quora, Medium, Reddit, Stack Exchange, wikiHow). We use various NLP techniques to understand these differences. For example, n-grams, emotion recognition, and question answering results indicate that Medium, Quora, and Stack Exchange are appropriate venues for those looking to transition from smoking to vaping. Other platforms (Reddit, wikiHow) are more for vaping hobbyists and may not sufficiently dissuade youth vaping. Conversely, Wikipedia may exaggerate vaping harms, dissuading smokers from transitioning. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to design informational tools to reinforce or mitigate vaping (mis)perceptions online.

We investigate how representations of Syrian refugees (2011-2021) differ across US partisan news outlets. We analyze 47,388 articles from the online US media about Syrian refugees to detail differences in reporting between left- and right-leaning media. We use various NLP techniques to understand these differences. Our polarization and question answering results indicated that left-leaning media tended to represent refugees as child victims, welcome in the US, and right-leaning media cast refugees as Islamic terrorists. We noted similar results with our sentiment and offensive speech scores over time, which detail possibly unfavorable representations of refugees in right-leaning media. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to intervene around refugee representations, and design communications campaigns that improve the way society sees refugees and possibly aid refugee outcomes.

In this paper, we investigate how vaping is framed differently (2008-2021) between US news and social media. We analyze 15,711 news articles and 1,231,379 Facebook posts about vaping to study the differences in framing between media varieties. We use word embeddings to provide two-dimensional visualizations of the semantic changes around vaping for news and for social media. We detail that news media framing of vaping shifted over time in line with emergent regulatory trends, such as; flavored vaping bans, with little discussion around vaping as a smoking cessation tool. We found that social media discussions were far more varied, with transitions toward vaping both as a public health harm and as a smoking cessation tool. Our cloze test, dynamic topic model, and question answering showed similar patterns, where social media, but not news media, characterizes vaping as combustible cigarette substitute. We use n-grams to detail that social media data first centered on vaping as a smoking cessation tool, and in 2019 moved toward narratives around vaping regulation, similar to news media frames. Overall, social media tracks the evolution of vaping as a social practice, while news media reflects more risk based concerns. A strength of our work is how the different techniques we have applied validate each other. Stakeholders may utilize our findings to intervene around the framing of vaping, and may design communications campaigns that improve the way society sees vaping, thus possibly aiding smoking cessation; and reducing youth vaping.