15 research outputs found
Winter is here: summarizing Twitter streams related to pre-scheduled events
Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.Published versio
Detecting frames in news headlines and its application to analyzing news framing trends surrounding U.S. gun violence
Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as “frames”, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news
headlines related to the issue of gun violence in the United States. This Gun Violence Frame
Corpus (GVFC) was curated and annotated by
journalism and communication experts. Our
proposed approach sets a new state-of-the-art
performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.Published versio
Challenges in measuring bias via open-ended language generation
Googlehttps://aclanthology.org/2022.gebnlp-1.9/First author draf
Resolving pronouns in Twitter streams: context can help!
Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event. Naturally, many tweets make pronominal reference to these people/characters. We propose an algorithm for resolving personal pronouns that make reference to people involved in an event, in tweet streams collected during the event.Published versio
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
Knowledge Graph (KG)-to-Text generation aims at generating fluent
natural-language text that accurately represents the information of a given
knowledge graph. While significant progress has been made in this task by
exploiting the power of pre-trained language models (PLMs) with appropriate
graph structure-aware modules, existing models still fall short of generating
faithful text, especially when the ground-truth natural-language text contains
additional information that is not present in the graph. In this paper, we
develop a KG-to-text generation model that can generate faithful
natural-language text from a given graph, in the presence of noisy reference
text. Our framework incorporates two core ideas: Firstly, we utilize
contrastive learning to enhance the model's ability to differentiate between
faithful and hallucinated information in the text, thereby encouraging the
decoder to generate text that aligns with the input graph. Secondly, we empower
the decoder to control the level of hallucination in the generated text by
employing a controllable text generation technique. We evaluate our model's
performance through the standard quantitative metrics as well as a
ChatGPT-based quantitative and qualitative analysis. Our evaluation
demonstrates the superior performance of our model over state-of-the-art
KG-to-text models on faithfulness
OpenFraming: open-sourced tool for computational framing analysis of multilingual data
When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called “frames,” and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user’s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online at http://www.openframing.org, via an API http://www.openframing.org:5000/docs/, or through our GitHub page https://github.com/vibss2397/openFraming.Published versio