2020 Kaleidoscope
Deciphering Election News Patterns [Week 5, 11/1/2019]
Nov 1· 4 min read
This newsletter is a weekly publication showcasing two simple applications of SumUp’s platform: an analysis of news pertaining to the 2020 elections and a technical review of research published on Computational linguistics.
2020 Election: Last week’s news coverage

What topics are most emphasized across the mainstream media’s coverage of the 2020 presidential election?
We ran 172 articles from 13 news feeds (in annex) through the SumUp platform. All information used is freely and easily available. We didn’t apply any filters. Keep in mind that these results are only representative of the 13 news sources listed at the bottom of this article.
This blog is not expressing an opinion but rather highlights the most relevant topics in the recent news coverage.
SumUp 2020 key topics on 11/1/2019, by order of relevance
Topic 1: Joe Biden/Elizabeth Warren
Explanation: Related to mentions in very different contexts, campaigning, Preside Trump releasing transcripts, Iowa pollsTopic 2: Donald Trump/Impeachment
Explanation: President Trump and impeachment appearing separately (campaigning in Chicago) or together with the impeachment inquiryTopic 3: Pete Buttigieg
Explanation: Mentioned in the context of general campaigningTopic 4: Wealth Tax
Explanation: Related to Elizabeth Warren proposal and constitutional authority to vote wealth taxTopic 5: Health Care
Explanation: Mentioned in relation to campaigning and more generally related to companies health care offeringTopic 6: Democratic Primary
Explanation: Several mentions of upcoming primaries and campaigningTopic 7: South Carolina/Iowa/New Hampshire
Explanation: General campaigningTopic 8: Cory Booker/Kamala Harris/Amy Klobuchar
Explanation: General campaigning
These topics are only representative of the key subjects appearing in the sources of information reviewed by Nucleus. A more refined interpretation, in terms of content or order of importance, is left to the reader and could easily be pursued with the Nucleus platform. In order to facilitate that analysis, we added the actual Nucleus analysis to the annex, providing further measure of sentiment attached to each of these topics, or a summary explanation of each topic.
Annex 1 Election 2020 news sources:
https://www.theguardian.com/us-news/us-elections-2020 https://fivethirtyeight.com/politics/elections/ https://www.chicagotribune.com/politics/elections/ https://www.politico.com/news/2020-elections https://www.cnn.com/specials/politics/2020-election-coverage https://time.com/5675691/2020-election-candidates-website-disability-accessibility/ https://www.newyorker.com/tag/2020-election https://www.foxnews.com/category/politics/2020-presidential-election https://www.usatoday.com/search/?q=2020%20democratic%20elections https://www.washingtonexaminer.com/tag/2020-elections https://www.washingtontimes.com/elections/ https://www.vox.com/2020-presidential-election https://www.breitbart.com/tag/2020-election/
Computational Linguistic Research Week of 11/1/2019
Leveraging its text analytics platform, SumUp has designed a simple method to rank recent research publications, purely based on their relation to the top topics extracted from these publications. Creating a corpus composed of all recent publications (all publications published on arxiv.org over the last week), Nucleus extracts 8 key topics representative of that research corpus. Nucleus then identifies the top documents related to these topics. SumUp ranks articles, according to the number of times each article is mentioned in the 8 extracted topics.
Using the corpus composed of articles published during the week of the 11th October 2019, the following 8 key topics are extracted:
[Natural language processing] [Neural net/speech recognition][Pre-trained language modeling][social media] [question answering] [training] [signal processing] [basic level categories]
Using SumUp ranking methodology, the following articles are most representative of the top 8 topics of the week of 11/1/2019:
JOINTLY OPTIMAL DEREVERBERATION AND BEAMFORMING
Christoph Boeddeker, Tomohiro Nakatani, Keisuke Kinoshita, Reinhold Haeb-Umbach
[Neural net/speech recognition][signal processing]
Evaluation of Sentence Embedding Models for Natural Language Understanding Problems in Russian
Dmitry Popov, Alexander Pugachev, Polina Svyatokum, Elizaveta Svitanko, Ekaterina Artemova
[question answering] [training]
TRANSFORMER-TRANSDUCER: END-TO-END SPEECH RECOGNITION WITH SELF-ATTENTION
Ching-Feng Yeh, Jay Mahadeokar , Kaustubh Kalgaonkar, Yongqiang Wang,
Duc Le, Mahaveer Jain, Kjell Schubert, Christian Fuegen, Michael L. Seltzer
[Neural net/speech recognition][signal processing]
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
Marzieh Mozafari, Reza Farahbakhsh, and Nobel Crespi
[social media][training]
On the Cross-lingual Transferability of Monolingual Representations
Mikel Artetxe, Sebastian Ruder, Dani Yogatama
[Pre-trained language modeling] [question answering]
All of the other articles published during the period under review are individually less relevant to the top topics.
