| 1 |
Within and Beyond Stereotypes of Arab Women: A Corpus-based Approach to Jordanian Women’s Portrayal in English Digital News
|
|
|
|
In: Journal of International Women's Studies (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Seeing our language: The effects of media representation on Scottish Gaelic learners
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Curlie Dataset - Language-agnostic Website Embedding and Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Curlie Dataset - Language-agnostic Website Embedding and Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Curlie Dataset - Language-agnostic Website Embedding and Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Curlie Dataset - Language-agnostic Website Embedding and Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
A review on discourse studies concerning migrants in media publications from Brazil and South Africa: towards more Afro-Latin perspectives
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Una revisión sistemática de la literatura de las representaciones de la migración en Brasil y Reino Unido
|
|
|
|
In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 71, 2022 (Ejemplar dedicado a: Discursos de odio en comunicación: Investigaciones y propuestas), pags. 49-61 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Semantic (Orbital) Sweep - Knowledge modeling and Semantic technology to clean Earth orbit and make spaceflight safer ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Semantic (Orbital) Sweep - Knowledge modeling and Semantic technology to clean Earth orbit and make spaceflight safer ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Essays on Representation Learning for Political Science Research
|
|
|
|
Abstract:
This dissertation consists of three papers about leveraging representation learning for political science research. Representation learning refers to techniques that learn a mapping between input data and a feature vector or tensor with respect to a task, such as classification or regression. These vectors or tensors capture abstract and relevant concepts in the data, making it easier to extract information. In the three papers, I show how representation learning allows political scientists to work with complex data such as text and images effectively. In the first paper, I propose using word embeddings to calculate partisan associations from Twitter users' bios. It only requires that some users in the corpus of tweets use partisan words in their bios. Intuitively, the word embeddings learn associations between non-partisan and partisan words from bios and extend those associations to all users. I apply the method to a collection of users who tweeted about election incidents during the 2016 United States general election. Which partisan accounts get retweeted, favorited, and followed, and which partisan hashtags are used closely correlate with the partisan association scores. I also apply the method to users who tweeted about masks during the COVID-19 pandemic. I find that users with more Democratic-leaning partisan association scores are more likely to use health advocacy hashtags, such as #MaskUp. In the second paper, I look at the automated classification of observations with both images and text. Most state-of-the-art vision-and-language models are unusable for most political science research, as they require all observations to have both image and text and require computationally expensive pretraining. This paper proposes a novel vision-and-language framework called multimodal representations using modality translation, or MARMOT. MARMOT presents two methodological contributions: it constructs representations for observations missing image or text, and it replaces computationally expensive pretraining with modality translation. Modality translation learns the patterns between images and their captions. MARMOT outperforms an ensemble text-only classifier in 19 of 20 categories in multilabel classifications of tweets reporting election incidents during the 2016 U.S. general election. MARMOT also shows significant improvements over the results of benchmark multimodal models on the Hateful Memes dataset, improving the best accuracy and area under the receiver operating characteristic curve (AUC) set by VisualBERT from 0.6473 to 0.6760 and 0.7141 to 0.7530, respectively. In the third paper, I turn to the issue of computationally studying language usage evolution over time. The corpora that political scientists typically work with are much smaller than the extensive corpora used in natural language processing research. Training a word embedding space over each period, the usual approach to studying language usage evolution, worsens the problem by splitting up the corpus into even smaller corpora. This paper proposes a framework that uses pretrained and non-pretrained embeddings to learn time-specific word embeddings, called the pretrained-augmented embeddings (PAE) framework. In the first application, I apply the PAE framework to a corpus of New York Times text data spanning several decades. The PAE framework matches human judgments of how specific words evolve in their usage much more closely than existing methods. In the second application, I apply the PAE framework to a corpus of tweets written during the COVID-19 pandemic about masking. The PAE framework automatically detects shifts in discussions about specific events during the COVID-19 pandemic vis-a-vis the keyword of interest. ; PHD ; Political Science ; University of Michigan, Horace H. Rackham School of Graduate Studies ; http://deepblue.lib.umich.edu/bitstream/2027.42/169642/1/pywu_1.pdf
|
|
Keyword:
computational social science; computer vision; multimodal machine learning; natural language processing; Political Science; representation learning; social media; Social Sciences; Statistics and Numeric Data
|
|
URL: https://doi.org/10.7302/2687
https://hdl.handle.net/2027.42/169642
|
|
BASE
|
|
Hide details
|
|
|
| 12 |
Framing Race: An Analysis of Media Coverage of the Racially Motivated Murders of Emmett Till and Trayvon Martin
|
|
|
|
In: Honors Theses (2021)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Russia’s Futures, from Fairy Tales and Editorials to Kremlin Narratives: Prokhanov, Dugin, Surkov
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
30 Years after the Breakup of the USSR: Russia and Post-Soviet Europe, Narratives and Perceptions
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
30 Years after the Breakup of the USSR: Russia and Post-Soviet Europe, Narratives and Perceptions. Special Issue Introduction
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Mujeres intérpretes de lengua de signos en la TDT española ; Women sign language interpreters in Spanish DTT
|
|
López Sánchez, Gema. - : Centro de Normalización Lingüística de la Lengua de Signos Española, 2021
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Aboriginal and Torres Strait Islander people(s) in Australian print news: A corpus-based critical discourse analysis
|
|
Bray, Carly. - : Department of Linguistics, 2021. : Faculty of Arts and Social Sciences, School of Literature, Art and Media, 2021
|
|
BASE
|
|
Show details
|
|
|
| 18 |
L¿ANNESSIONE DELLA CRIMEA ALLA FEDERAZIONE RUSSA NELLE PAROLE DELLA STAMPA. PRATICHE DISCORSIVE E COSTRUZIONE DELL¿IDENTITÀ NAZIONALE
|
|
F. Volpi. - : Università degli Studi di Milano, 2021
|
|
BASE
|
|
Show details
|
|
|
| 19 |
GENDER INEQUALITY WITHIN A FAMILY: THE REPRESENTATION OF WOMEN’S NOVEL IN SOCIAL MEDIA
|
|
|
|
In: LiNGUA: Jurnal Ilmu Bahasa dan Sastra; Vol 16, No 1 (2021): LiNGUA; 111 - 124 ; 2442-3823 ; 1693-4725 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
News Media Representation of The Dakota Access Pipeline Protest (A Study Using Systemic Functional Linguistics)
|
|
|
|
In: http://rave.ohiolink.edu/etdc/view?acc_num=kent1594292005011941 (2020)
|
|
BASE
|
|
Show details
|
|
|
|
|
