Public opinion mining on Sochi-2014 Olympics

Abstract de la ponencia[EN] The requirements of evidence-based policymaking promote interest to realtime monitoring of public’s opinions on policy-relevant topics, and social media data mining allows diversification of information portfolio used by public administrators. This study discusses issues in public opinion mining with respect to extraction and analysis of information posted on Twitter about Sochi-2014 Olympic. It focuses on topics discussed on Twitter and sentiment analysis of tweets about the Games. Final database contained 613,333 tweets covering time span from November 1, 2013 until March 31, 2014. Using hash tags the data were classified into the following categories: Anticipation of the Games (9%), Cheering of the teams (31%), News (6%), Events (11%), Sports (18%), and Problems & Politics (15%). Research reveals considerable differences in the outcomes of machine sentiment classifiers: Deeply Moving, Pattern, and SentiStrength. SentiStrength produced the most suitable results in terms of minimization of incorrectly classified tweets. Methodological implications and directions for future research are discussed.Kirilenko, A.; Stepchenkova, S. (2016). Public opinion mining on Sochi-2014 Olympics. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 122-122. https://doi.org/10.4995/CARMA2016.2015.3102OCS12212

The Impacts of COVID-19 on Tourists’ Emotions Expressed from TripAdvisor Reviews: Emotion Detection of Travel Experiences in Yellowstone National Park

The coronavirus (COVID-19) pandemic threatens the health and lives of millions of people all over the world. The spread of the virus hit the tourism industry heavily. Many destinations are forced to shut down, and the right to travel had been ceased in an unprecedented way, altering people’s travel intentions and preferences. One observed trend is a higher percentage of travelers electing trips to natural areas. While travelers’ emotions have been studied thoroughly, little is known if and how the pandemics and higher than usual medical risks associated with travel changed them. The goal of this study is to investigate and detect changes in travelers’ emotions in the COVID-19 year. The additional objective is to cross-comparison the changes among tourists from different continents. The study used the automated sentiment analysis to detect emotions of multi-year TripAdvisor reviews of Yellowstone National Park

How Sharing Information Can Garble Experts’ Advice

We model the strategic provision of advice in environments where a principal's optimal action depends on an unobserved, binary state of interest. Experts receive signals about the state and each recommends an action. The principal and all experts dislike making errors in their decision and recommendations, respectively, but may have different costs of different errors. Is it in the principal's interest to let experts share information? Although sharing improves experts' ability to avoid errors, we identify a simple environment in which any principal, regardless of how he trades off the different errors, is worse off if he permits information sharing

Instagram travel influencers coping with covid-19 travel disruption

A significant portion of today’s marketing is done through social media influencers, that is, through bloggers with established online credibility in a certain area who are recognized and followed by a sizable online audience. In the travel and hospitality industry, the influencer marketing is primarily done through Instagram due to its emphasis on visual images rather than texts. Covid-19 related travel restrictions and shrinking social media advertisement in travel industry have heavily impacted travel influencers, reducing their income and forcing many out of business. We present the outcomes of a study of the top 150 online travel influencers. The analysis is based on 11,000 photographs and texts published in two time periods before and during COVID-19 epidemics. We found that COVID-19 has induced transformative changes in the influencers’ online behavior reflected in changes in their posting frequency, themes, and expressed emotions

Theme Park Visitor Experience and Satisfaction: A Case of TripAdvisor Reviews of Three Theme Parks in Orlando

The significant growth of user-generated content on social media has received a lot of attention and big data has been acknowledged as a key source for understanding customer behaviors in tourism. Most of the existing research of this sort has focused on tourist destination and hospitality, but not on theme parks. The purpose of the study was to empirically identify the experience structure and satisfaction of visitors to three theme parks in Orlando by using TripAdvisor reviews. A total of 40,978 online customer reviews from 2016 were utilized for sentiment analysis and text analysis. The findings revealed that visitors rate the three theme parks differently and both unique and similar experience themes were identified. We propose that similarity and uniqueness are crucial for theme park satisfaction, where appealing to tourist needs is important for establishing distinctiveness in areas of similarity and creativity is important for enhancing uniqueness

Tourism impact on gender and minority equality: comparison of survey, census, and industry data

Tourism is widely credited as an economic driver for communities, but the role of tourism industry in promoting social and cultural human development goals is less researched and frequently disputed. The majority of existing studies are following the coarse scale. The goal of this study is to investigate the relationship between the development of tourism sector and progress towards the UNWTO equality goals on a fine scale of one US state with the objective of comparing the available indicators of gender and minority equality on a county basis. The study combines the objective data from industry and the US census with survey data on local community perceptions on tourism industry. We found that overall the equality indicators follow the Kuznets type U-shaped model. This result is supported with the survey of public perceptions on tourism which suggests a positive outlook of the local communities on tourism industry effects on gender and race equality. This positive outlook is however not fully shared by women, Black minority, and the youngest population, suggesting a need for future studies those groups

‘Flash Crash’: The first market crash in the era of algorithms and automated trading

How non-designated intraday intermediaries responded in the E-mini S&P 500 futures market crash on 6 May 2010 - by Andrei Kirilenko, Albert Kyle, Mehrdad Samadi, and Tugkan Tuzu

The flash crash: The impact of high frequency trading on an electronic market

The Flash Crash, a brief period of extreme market volatility on May 6, 2010, raised questions about the current structure of the U.S. financial markets. We use audit-trail data to describe the structure of the E-mini S&P 500 stock index futures market on May 6. We ask three questions. How did High Frequency Traders (HFTs) trade on May 6? What may have triggered the Flash Crash? What role did HFTs play in the Flash Crash? We conclude that HFTs did not trigger the Flash Crash, but their responses to the unusually large selling pressure on that day exacerbated market volatility

Trading networks

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139950/1/ectj12090-sup-0001-onlineappendix.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139950/2/ectj12090_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139950/3/ectj12090.pd