Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media最新文献

Negative Associations in Word Embeddings Predict Anti-black Bias across Regions-but Only via Name Frequency. 词嵌入中的负关联预测跨地区的反黑人偏见——但仅通过名字频率。

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19399

Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt

{"title":"Negative Associations in Word Embeddings Predict Anti-black Bias across Regions-but Only via Name Frequency.","authors":"Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt","doi":"10.1609/icwsm.v16i1.19399","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19399","url":null,"abstract":"The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., 'pleasant' and 'unpleasant' words). We show that anti-Black WEAT estimates from geo-tagged social media data at the level of metropolitan statistical areas strongly correlate with several measures of racial animus-even when controlling for sociodemographic covariates. However, we also show that every one of these correlations is explained by a third variable: the frequency of Black names in the underlying corpora relative to White names. This occurs because word embeddings tend to group positive (negative) words and frequent (rare) words together in the estimated semantic space. As the frequency of Black names on social media is strongly correlated with Black Americans' prevalence in the population, this results in spuriously high anti-Black WEAT estimates wherever few Black Americans live. This suggests that research using the WEAT to measure bias should consider term frequency, and also demonstrates the potential consequences of using black-box models like word embeddings to study human cognition and behavior.","PeriodicalId":74525,"journal":{"name":"Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media","volume":"16 ","pages":"1419-1424"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147343/pdf/nihms-1842382.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9399665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Correcting Sociodemographic Selection Biases for Population Prediction from Social Media. 从社交媒体中纠正人口预测的社会人口选择偏差。

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2022-05-31

Salvatore Giorgi, Veronica E Lynn, Keshav Gupta, Farhan Ahmed, Sandra Matz, Lyle H Ungar, H Andrew Schwartz

{"title":"Correcting Sociodemographic Selection Biases for Population Prediction from Social Media.","authors":"Salvatore Giorgi, Veronica E Lynn, Keshav Gupta, Farhan Ahmed, Sandra Matz, Lyle H Ungar, H Andrew Schwartz","doi":"","DOIUrl":"","url":null,"abstract":"Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population - a \"selection bias\". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. In this two-part study, we first evaluate standard, \"out-of-the-box\" restratification techniques, finding they provide no improvement and often even degraded prediction accuracies across four tasks of esimating U.S. county population health statistics from Twitter. The core reasons for degraded performance seem to be tied to their reliance on either sparse or shrunken estimates of each population's socio-demographics. In the second part of our study, we develop and evaluate Robust Poststratification, which consists of three methods to address these problems: (1) estimator redistribution to account for shrinking, as well as (2) adaptive binning and (3) informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods leads to significant improvement in prediction accuracies over the standard restratification approaches. Taken together, Robust Poststratification enables state-of-the-art prediction accuracies, yielding a 53.0% increase in variance explained (R 2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks.","PeriodicalId":74525,"journal":{"name":"Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media","volume":"16 1","pages":"228-240"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714525/pdf/nihms-1842768.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35254726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory. 利用双向长短期记忆对社交媒体上的少数群体压力披露进行分类。

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2022-05-31

Cory J Cascalheira, Shah Muhammad Hamdi, Jillian R Scheer, Koustuv Saha, Soukaina Filali Boubrahimi, Munmun De Choudhury

引用次数: 0

Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory 双向长短期记忆对少数民族社交媒体压力披露的分类研究

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19390

C. Cascalheira, S. M. Hamdi, Jillian R. Scheer, Koustuv Saha, S. F. Boubrahimi, M. Choudhury

引用次数: 3

Tweet Classification to Assist Human Moderation for Suicide Prevention. 推文分类协助人类适度自杀预防。

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2021-06-04 Epub Date: 2021-05-22

Ramit Sawhney, Harshit Joshi, Alicia Nobles, Rajiv Ratn Shah

{"title":"Tweet Classification to Assist Human Moderation for Suicide Prevention.","authors":"Ramit Sawhney, Harshit Joshi, Alicia Nobles, Rajiv Ratn Shah","doi":"","DOIUrl":"","url":null,"abstract":"Social media platforms are already engaged in leveraging existing online socio-technical systems to employ just-in-time interventions for suicide prevention to the public. These efforts primarily rely on self-reports of potential self-harm content that is reviewed by moderators. Most recently, platforms have employed automated models to identify self-harm content, but acknowledge that these automated models still struggle to understand the nuance of human language (e.g., sarcasm). By explicitly focusing on Twitter posts that could easily be misidentified by a model as expressing suicidal intent (i.e., they contain similar phrases such as \"wanting to die\"), our work examines the temporal differences in historical expressions of general and emotional language prior to a clear expression of suicidal intent. Additionally, we analyze time-aware neural models that build on these language variants and factors in the historical, emotional spectrum of a user's tweeting activity. The strongest model achieves high (statistically significant) performance (macro F1=0.804, recall=0.813) to identify social media indicative of suicidal intent. Using three use cases of tweets with phrases common to suicidal intent, we qualitatively analyze and interpret how such models decided if suicidal intent was present and discuss how these analyses may be used to alleviate the burden on human moderators within the known constraints of how moderation is performed (e.g., no access to the user's timeline). Finally, we discuss the ethical implications of such data-driven models and inferences about suicidal intent from social media. Content warning: this article discusses self-harm and suicide.","PeriodicalId":74525,"journal":{"name":"Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media","volume":" ","pages":"609-620"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843106/pdf/nihms-1774843.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39627521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Well-Being Depends on Social Comparison: Hierarchical Models of Twitter Language Suggest That Richer Neighbors Make You Less Happy. 幸福取决于社会比较:推特语言的等级模型表明，更富有的邻居会让你更不快乐。

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2021-01-01 DOI: 10.1609/icwsm.v15i1.18132

Salvatore Giorgi, Sharath Chandra Guntuku, Johannes C Eichstaedt, Claire Pajot, H Andrew Schwartz, Lyle H Ungar

{"title":"Well-Being Depends on Social Comparison: Hierarchical Models of Twitter Language Suggest That Richer Neighbors Make You Less Happy.","authors":"Salvatore Giorgi, Sharath Chandra Guntuku, Johannes C Eichstaedt, Claire Pajot, H Andrew Schwartz, Lyle H Ungar","doi":"10.1609/icwsm.v15i1.18132","DOIUrl":"https://doi.org/10.1609/icwsm.v15i1.18132","url":null,"abstract":"Psychological research has shown that subjective well-being is sensitive to social comparison effects; individuals report decreased happiness when their neighbors earn more than they do. In this work, we use Twitter language to estimate the well-being of users, and model both individual and neighborhood income using hierarchical modeling across counties in the United States (US). We show that language-based estimates from a sample of 5.8 million Twitter users replicate results obtained from large-scale well-being surveys - relatively richer neighbors leads to lower well-being, even when controlling for absolute income. Furthermore, predicting individual-level happiness using hierarchical models (i.e., individuals within their communities) out-predicts standard baselines. We also explore language associated with relative income differences and find that individuals with lower income than their community tend to swear (f*ck, sh*t, b*tch), express anger (pissed, bullsh*t, wtf), hesitation (don't, anymore, idk, confused) and acts of social deviance (weed, blunt, drunk). These results suggest that social comparison robustly affects reported well-being, and that Twitter language analyses can be used to both measure these effects and shed light on their underlying psychological dynamics.","PeriodicalId":74525,"journal":{"name":"Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media","volume":"15 ","pages":"1069-1074"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099468/pdf/nihms-1854629.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9328583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Examining Peer-to-Peer and Patient-Provider Interactions on a Social Media Community Facilitating Ask the Doctor Services. 研究促进 "问医生 "服务的社交媒体社区上的点对点互动和患者与医疗服务提供者之间的互动。

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2020-06-01

Alicia L Nobles, Eric C Leas, Mark Dredze, John W Ayers

引用次数: 0

Examining Peer-to-Peer and Patient-Provider Interactions on a Social Media Community Facilitating Ask the Doctor Services 在促进医生咨询服务的社交媒体社区中检查点对点和患者-提供者互动

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2020-05-26 DOI: 10.1609/icwsm.v14i1.7315

A. Nobles, E. Leas, Mark Dredze, J. Ayers

引用次数: 10

Correcting Sociodemographic Selection Biases for Population Prediction from Social Media 纠正社会人口选择偏差对社会媒体人口预测的影响

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2019-11-10 DOI: 10.1609/icwsm.v16i1.19287

Salvatore Giorgi, Veronica E. Lynn, Keshav Gupta, F. Ahmed, S. Matz, Lyle Ungar, H. A. Schwartz

{"title":"Correcting Sociodemographic Selection Biases for Population Prediction from Social Media","authors":"Salvatore Giorgi, Veronica E. Lynn, Keshav Gupta, F. Ahmed, S. Matz, Lyle Ungar, H. A. Schwartz","doi":"10.1609/icwsm.v16i1.19287","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19287","url":null,"abstract":"Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population - a \"selection bias\". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. In this two-part study, we first evaluate standard, \"out-of-the-box\" restratification techniques, finding they provide no improvement and often even degraded prediction accuracies across four tasks of esimating U.S. county population health statistics from Twitter. The core reasons for degraded performance seem to be tied to their reliance on either sparse or shrunken estimates of each population's socio-demographics. In the second part of our study, we develop and evaluate Robust Poststratification, which consists of three methods to address these problems: (1) estimator redistribution to account for shrinking, as well as (2) adaptive binning and (3) informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods leads to significant improvement in prediction accuracies over the standard restratification approaches. Taken together, Robust Poststratification enables state-of-the-art prediction accuracies, yielding a 53.0% increase in variance explained (R 2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks.","PeriodicalId":74525,"journal":{"name":"Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media","volume":"41 1","pages":"228-240"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77499252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

A Social Media Study on the Effects of Psychiatric Medication Use 社交媒体对精神科药物使用影响的研究

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media Pub Date : 2019-06-07 DOI: 10.1609/icwsm.v13i01.3242

Koustuv Saha, Benjamin Sugar, J. Torous, B. Abrahao, Emre Kıcıman, M. Choudhury

{"title":"A Social Media Study on the Effects of Psychiatric Medication Use","authors":"Koustuv Saha, Benjamin Sugar, J. Torous, B. Abrahao, Emre Kıcıman, M. Choudhury","doi":"10.1609/icwsm.v13i01.3242","DOIUrl":"https://doi.org/10.1609/icwsm.v13i01.3242","url":null,"abstract":"Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to first assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Then, based on a stratified propensity score based causal analysis, we observe that use of specific drugs are associated with characteristic changes in an individual's psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.","PeriodicalId":74525,"journal":{"name":"Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media","volume":"23 1","pages":"440-451"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81546033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 70