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Şehir dışı harcamaların ve turist gezilerinin kredi kartı işlemsel verileri kullanılarak analizi
Credit card transaction data contains a vast amount of valuable information that can indicate consumer behaviour patterns and mark out human mobility. In this study we analyse the transactions carried out by a sample of 10.000 Istanbul-based customers of a Turkish bank to scrutinize expenditures incurred out of Istanbul. In our preliminary descriptive analysis, we examine the relation between demographic attributes and spending measures, as well as investigate the extent to which the population and the number of points of interest imply higher or lower credit card expenditure by visitors. We develop a methodology to extract tourist trips from consecutive credit card transactions. Subsequently, we implement a hierarchical clustering method to evaluate what the purpose of these trips might have been. Our results indicate 5 clusters of purpose: 'Leisure', 'Business', 'Acquisition', 'Visiting Friends and Relative' and 'Package Holiday'. The same clustering method is applied to segment provinces of Turkey based on which product and service categories visitors prefer. We deploy a number of predictive models to estimate tourist expenditure and whether a person would embark on a trip in the upcoming months. The predictive power of these models are generally moderate; nevertheless, several of the most useful predictors are behavioural or are related to previous trips, factors that have not been considered in literature.
Bayrak etrafında kimler toplanır? Milletlerin siyasi tutumları üzerindeki dış müdahalelerin etkilerini sosyal medya verileriyle analiz etmek
There is a common-sense that in times of foreign interventions, the country's political actors are likely to set aside their differences and support the state or the government for a period of time as a temporary reaction to that foreign intervention. This study focuses on the specific case of Iran-U.S. conflicts to investigate the effects of events such as U.S. sanctions and military interventions on political discourse among Iranian influencers on Twitter. The quantitative approach in this study utilizes Classical Natural Language Processing to measure Iranian tweeps' sentiment towards the state across the time. We have grouped Iranian Twitter Influencers by their political affiliations to analyze which political affiliations in Iran (e.g. Conservatives, Reformists, …) are more likely to rally around the flag in correspondence to foreign interventions and what categories of foreign interventions have more potential in stimulating them for such reactions. Keywords: foreign intervention, Iran, US, NLP, Twitter