2 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 2 / 2
Yayın Impact of novel coronavirus (COVID-19) on daily routines and air environment: evidence from Turkey(Springer, 2021-03) Ali, Hussain; Yılmaz, Gözde; Fareed, Zeeshan; Shahzad, Farrukh; Ahmad, MunirTurkish people are facing several problems because of the novel coronavirus (COVID-19), as the pandemic has brought about drastic changes to their daily routines. This study mainly investigates the impact of this pandemic on the daily routines of Turkish. It also unveils how COVID-19 affects the air environment. The adopted methods for data collection are based on open-ended questions and Facebook interviews as per recommended by QSR-International (2012). The sample of this study comprises of Turkish students as well as professional workers. The findings of the research show that there are eighteen different results of COVID-19 that have been identified according to the Turkish people’s daily routines. Results reveal that increasing unemployment, decrease in air contamination, high stress and depression, a slowdown in the economic growth, and the tourism industry are profoundly affected due to the COVID-19 in Turkey. Furthermore, on the one hand, the consequences of the pandemic are segregated into social problems and psychological issues in daily routines. On the other hand, they have shown a positive impact on the air environment. This study concludes that, amid the COVID-19 pandemic, the lives of the people in Turkey are subject to deterioration, while the air environment of Turkey is gradually improving.Yayın ComStreamClust: a communicative multi-agent approach to text clustering in streaming data(Springer Science and Business Media Deutschland GmbH, 2023-12) Najafi, Ali; Gholipour-Shilabin, Araz; Dehkharghani, Rahim; Mohammadpur-Fard, Ali; Asgari-Chenaghlu, MeysamTopic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel, multi-agent, communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g., the COVID-19 and the FA CUP. The proposed approach is parallelizable, and can simultaneously handle several data-point. The LaBSE sentence embedding is used to measure the semantic similarity between two tweets. ComStreamClust has been evaluated by several metrics such as keyword precision, keyword recall, and topic recall. Based on topic recall on different number of keywords, ComStreamClust obtains superior results when compared to the existing methods.
