Revealing the Reflections of the Pandemic by Investigating COVID-19 Related News Articles Using Machine Learning and Network Analysis

Year 2022, , 209 - 220, 30.04.2022

Ulya Bayram

https://doi.org/10.17671/gazibtd.949599

Cited By: 2

Abstract

Social media data can provide a general idea of people’s response towards the COVID-19 outbreak and its reflections, but it cannot be as objective as the news articles as a source of information. They are valuable sources of data for natural language processing research as they can reveal various paradigms about different phenomena related to the pandemic. This study uses a news collection spanning nine months from 2019 to 2020, containing COVID-19 related articles from various organizations around the world. The investigation conducted on the collection aims at revealing the repercussions of the pandemic at multiple levels. The first investigation discloses the most mentioned problems covered during the pandemic using statistics. Meanwhile, the second investigation utilizes machine learning to determine the most prevalent topics present within the articles to provide a better picture of the pandemic-induced issues. The results show that the economy was among the most prevalent problems. The third investigation constructs lexical networks from the articles, and reveals how every problem is related through nodes and weighted connections. The findings exhibit the need for more research using machine learning and natural language processing techniques on similar data collections to unveil the full repercussions of the pandemic.

Keywords

LDA, BERT, machine learning, news articles, natural language processing, network analysis

Pandeminin Yansımalarını Ortaya Çıkarmak için COVID-19 ile İlgili Gazete Makalelerinin Makine Öğrenimi ve Ağ Analizi Yöntemleri ile İncelenmesi

Abstract

Sosyal medya platformlarından elde edilmiş veriler, insanların COVID-19 pandemisine karşı gösterdiği tepkiler hakkında bilgi verse de gazete makaleleri kadar objektif bir şekilde bilgi içeremezler. Pandemi sürecinde yayınlanmış makaleler genel halkın yaşanılan krizden nasıl etkilendiği hakkında bilgi vermekle birlikte, aynı zamanda siyasi ve daha farklı alanlardaki etkilerden de bahsederler. Bu makaleler, pandemiyle ilgili çok farklı paradigmaları içermeleri sebebiyle doğal dil işleme araştırmaları için faydalı veri kaynaklarıdır. Bu çalışmada, 2019 ve 2020 yıllarındaki COVID-19 ile ilgili uluslararası haber organizasyonları tarafından dokuz ay boyunca yayınlanmış gazete makaleleri koleksiyonunu kullanmaktadır. Bu koleksiyon üzerine üç kademeli bir inceleme çalışması uygulayarak pandeminin sebep olduğu sonuçları farklı derecelerde açığa çıkarmayı amaçlamaktadır. İlk çalışma, kelime istatistiklerini kullanarak pandemi sürecinde makalelerde en çok bahsedilen problemleri ortaya çıkarır. İkinci çalışma ise, makalelerden pandeminin sebep olduğu problemleri daha iyi ortaya çıkarmak için makine öğrenimi yöntemleriyle konu modelleme yapar. Sonuçlara göre en sık bahsedilen pandemi sebepli problemlerden biri ekonomik olanlardır. Üçüncü çalışma da gazete makalelerinden sözlüksel ağ oluşturarak düğüm ve ağırlıklı bağlantılar üzerinden pandemi sürecinde birçok problemin nasıl bağlantılı olduğunu gösterir. Buluntulara göre makine öğrenimi ve doğal dil işleme yöntemleri ile benzer veri setleri üzerinde pandeminin tüm etkilerinin daha çok araştırılması gerektiği görülmektedir.

Keywords

LDA, BERT, BERT, makine öğrenimi, gazete makaleleri, doğal dil işleme, ağ analizi

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