Papers by Danae Sánchez Villegas
Analyzing Online Political Advertisements (2021.findings-acl)
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| Challenge: | Online political advertising is an integral part of modern digital election campaigning. |
| Approach: | They propose to use textual and visual information from pre-trained neural models to infer the political ideology of an ad sponsor and identify whether the sponsor is an official political party or a third-party organization. |
| Outcome: | The proposed approach outperforms state-of-the-art methods for generic commercial ad classification and linguistic analysis to study the characteristics of political ads discourse. |
Point-of-Interest Type Inference from Social Media Text (2020.aacl-main)
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| Challenge: | Using a dataset of 200,000 English tweets, we can predict the type of the place from which a tweet was sent from. |
| Approach: | They propose to analyze a dataset of 200,000 tweets from 2,761 points-of-interest in the U.S. and train classifiers to predict the type of the location a tweet was sent from. |
| Outcome: | The proposed method can predict the type of the location a tweet was sent from and reach a macro F1 of 43.67 across eight classes. |
Point-of-Interest Type Prediction using Text and Images (2021.emnlp-main)
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| Challenge: | Prior efforts in POI type prediction focus on text without taking visual information into account. |
| Approach: | They propose to use multimodal information from text and images to infer the type of a place from where a social media post was shared. |
| Outcome: | The proposed method outperforms the state-of-the-art method for POI type prediction based on text-only methods and sheds light on cross-modal interactions and limitations. |
Analyzing Political Parody in Social Media (2020.acl-main)
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| Challenge: | Parody is a figurative device used to imitate an entity for comedic or critical purposes. |
| Approach: | They propose a dataset of tweets from real politicians and their corresponding parody accounts to run supervised machine learning models for automatic classification. |
| Outcome: | The proposed models predict political parody tweets with 90% accuracy . they also identify the markers of parody through a linguistic analysis . |