Detecting Anchors' Opinion in Hinghlish News Delivery
- URL: http://arxiv.org/abs/2204.02155v1
- Date: Tue, 5 Apr 2022 12:26:46 GMT
- Title: Detecting Anchors' Opinion in Hinghlish News Delivery
- Authors: Siddharth Sadhwani, Nishant Grover, Md Akhtar, Tanmoy Chakraborty
- Abstract summary: We propose a novel task of anchors' opinion detection in debates.
We curate code-mixed news debates and develop the ODIN dataset.
A total of 2054 anchors' utterances in the dataset are marked as opinionated or non-opinionated.
- Score: 22.98110639419913
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Humans like to express their opinions and crave the opinions of others.
Mining and detecting opinions from various sources are beneficial to
individuals, organisations, and even governments. One such organisation is news
media, where a general norm is not to showcase opinions from their side.
Anchors are the face of the digital media, and it is required for them not to
be opinionated. However, at times, they diverge from the accepted norm and
insert their opinions into otherwise straightforward news reports, either
purposefully or unintentionally. This is primarily seen in debates as it
requires the anchors to be spontaneous, thus making them vulnerable to add
their opinions. The consequence of such mishappening might lead to biased news
or even supporting a certain agenda at the worst. To this end, we propose a
novel task of anchors' opinion detection in debates. We curate code-mixed news
debates and develop the ODIN dataset. A total of 2054 anchors' utterances in
the dataset are marked as opinionated or non-opinionated. Lastly, we propose
DetONADe, an interactive attention-based framework for classifying anchors'
utterances and obtain the best weighted-F1 score of 0.703. A thorough analysis
and evaluation show many interesting patterns in the dataset and predictions.
Related papers
- DocNet: Semantic Structure in Inductive Bias Detection Models [0.4779196219827508]
We present DocNet, a novel, inductive, and low-resource document embedding and political bias detection model.
We demonstrate that the semantic structure of news articles from opposing political sides, as represented in document-level graph embeddings, have significant similarities.
arXiv Detail & Related papers (2024-06-16T14:51:12Z) - Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in
News Reporting [7.8192232188516115]
We study to which degree media balances news reporting and affects consumers through event inclusion or omission.
We first introduce the task of detecting both partisan and counter-partisan events.
Our findings highlight both the ways in which the news subtly shapes opinion and the need for large language models.
arXiv Detail & Related papers (2023-10-28T17:50:13Z) - Towards Corpus-Scale Discovery of Selection Biases in News Coverage:
Comparing What Sources Say About Entities as a Start [65.28355014154549]
This paper investigates the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora.
We show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide.
arXiv Detail & Related papers (2023-04-06T23:36:45Z) - Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S.
News Headlines [63.52264764099532]
We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022.
We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs.
Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
arXiv Detail & Related papers (2023-03-28T03:31:37Z) - Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases.
We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions.
Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
arXiv Detail & Related papers (2023-01-14T18:58:42Z) - GREENER: Graph Neural Networks for News Media Profiling [24.675574340841163]
We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias.
Our main focus is on modeling the similarity between media outlets based on the overlap of their audience.
Prediction accuracy is found to improve by 2.5-27 macro-F1 points for the two tasks.
arXiv Detail & Related papers (2022-11-10T12:46:29Z) - NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias [54.89737992911079]
We propose a new task, a neutral summary generation from multiple news headlines of the varying political spectrum.
One of the most interesting observations is that generation models can hallucinate not only factually inaccurate or unverifiable content, but also politically biased content.
arXiv Detail & Related papers (2022-04-11T07:06:01Z) - Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty
in Election Polls [56.8172499765118]
We discuss potential sources of bias in nowcasting and forecasting.
Concepts are presented to attenuate the issue of falsely perceived accuracy.
One key idea is the use of Probabilities of Events instead of party shares.
arXiv Detail & Related papers (2021-04-28T07:02:24Z) - Political audience diversity and news reliability in algorithmic ranking [54.23273310155137]
We propose using the political diversity of a website's audience as a quality signal.
Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards.
arXiv Detail & Related papers (2020-07-16T02:13:55Z) - Out of the Echo Chamber: Detecting Countering Debate Speeches [18.321466611103684]
We study the problem in the context of debate speeches.
We aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it.
We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance.
arXiv Detail & Related papers (2020-05-03T18:02:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.