Bullish Sentiment on Price Upward Trend : A Netnographic Study of Cryptocurrency Communities

Cryptocurrency as a digital decentralized currency has attracted many investors and obtained a lot of support from communities. Previous studies have concluded that there were indeed connections between community sentiment and cryptocurrency price movement. However, most of the research were conducted using sophisticated methods that difficult to utilized by cryptocurrency investors. This research objective was to find practical ways to identify bullish sentiment during price upward trend especially during the Covid-19 omicron variant outbreak that started in the last quarter of 2021. Netnography method was used as qualitative approach for this research to get insight from cryptocurrency communities. LunarCrush web application as a more user-friendly tool, was being used to analyze bullish sentiment and price data. During December 2021 until January 2022, 303 price upward trend data from 264 coins had been collected and analyzed. The result of this research revealed 5 categories of bullish sentiment messages from top influencers which are community booster, official information, project updates, achievement, and trading plan. However, it can be concluded that price movements were not always related with bullish sentiment. Thus, bullish sentiment should not be used as the sole factor to identify price upward trends. Furthermore, investors should join the cryptocurrency community to understand the characteristics of bullish sentiment and not just rely on data from monitoring tools. Interestingly, there were no Covid-19 related topics on bullish sentiment collected. Hence, it did not necessarily need to publish good news related to Covid-19 handling to create bullish sentiment among the investors

Bullish Sentiment on Price Upward Trend: A Netnographic Study of Cryptocurrency Communities

Cryptocurrency as a digital decentralized currency has attracted many investors and obtained a lot of support from communities. Previous studies have concluded that there were indeed connections between community sentiment and cryptocurrency price movement. However, most of the research was conducted using sophisticated methods that are difficult to utilize by cryptocurrency investors. This research objective was to find practical ways to identify bullish sentiment during price upward trend especially during the Covid-19 omicron variant outbreak that started in the last quarter of 2021. Netnography method was used as a qualitative approach for this research to get insight from cryptocurrency communities. LunarCrush web application as a more user-friendly tool, was being used to analyze bullish sentiment and price data. During December 2021 until January 2022, 303 price upward trend data from 264 coins had been collected and analyzed. The result of this research revealed 5 categories of bullish sentiment messages from top influencers which are community booster, official information, project updates, achievement, and trading plan. However, it can be concluded that price movements were not always related to bullish sentiment. Thus, bullish sentiment should not be used as the sole factor to identify price upward trends. Furthermore, investors should join the cryptocurrency community to understand the characteristics of bullish sentiment and not just rely on data from monitoring tools. Interestingly, there were no Covid-19 related topics on bullish sentiment collected. Hence, it did not necessarily need to publish good news related to Covid-19 handling to create bullish sentiment among the investors

Crisis Response Strategies During Cryptocurrency Crash: A Netnographic Studies of Lunatics Community

Cryptocurrency is a speculative investment due to its volatility which could result in significant returns but also could end in crashes. Terra blockchain collapsed when its stable coin UST failed to maintain its peg to 1 USD and caused its sister coin LUNA to drop by more than 90% only in a few days. Terra LUNA itself has gained success and attracted many investors that became a strong community called Lunatics. Using Netnography; this study tried to observe crisis response strategies from Do Kwon, founderand CEO of Terraform Labs, and from Terra LUNA official Twitter account during the crash. Also, this study used community sentiment as an indicator to measure the success of the strategies. In addition, this study observed the interaction of the communityduring the crash period and how they overcome the crisis together. The results show that mortification and corrective action are the most effective strategy to generate positive sentiment. However, denials toward rumors cause more negative sentiment within the community. Despite the recovery plan from the Terra network, the Lunatics community also has its ways of recovering from the crisis. This study also revealed that community influencers roles are crucial in controlling rumors during the crisis

Most valued factors in rural tourism: An analysis of Portuguese customer comments on a booking platform

This study aims to discover and analyze the most valued factors in rural tourism. An analysis of a sample with 9,939 comments obtained from the booking platform Booking.com on stays in rural tourism establishments located in Portugal is presented. Using text mining techniques and topic modeling in order to find the most valued factors, word clouds were obtained that allow a quick and intuitive analysis of the data. The LDA algorithm allowed to extract ten topics coherent with the word clouds. The results point out that breakfast, the friendliness of the staff, the hosts and the outdoor space are the most relevant factors for guests. These results have implications for the management of rural tourism units as they identify the factors to be taken into account by those in charge of these units

Green energy: identifying development trends in society using Twitter data mining to make strategic decisions

This study analyzes Twitter’s contribution to green energy. More than 200,000 global tweets sent during 2020 containing the terms “green energy” OR “greenenergy” were analyzed. The tweets were captured by web scraping and processed using algorithms and techniques for the analysis of massive datasets from social networks. In particular, relationships between users (through mentions) were determined according to the Louvain multilevel algorithm to identify communities and analyze global (density and centralization) and node-level (centrality) metrics. Subsequently, the content of the conversation was subject to semantic analysis (co-occurrence of the most relevant words), hashtag analysis (frequency analysis), and sentiment analysis (using the Vader model). The results reveal nine main communities and their leaders, as well as three main topics of conversation and the emotional state of the digital discussion. The main communities revolve around politics, socioeconomic issues, and environmental activism, while the conversations, which have developed mostly in positive terms, focus on green energy sources and storage, being aligned with the main communities identified, i.e., on political, socioeconomic, and climate change issues. Although most of the conversations have been about socioeconomic issues, the presence of leading company accounts was minor. The main aim of this work is to take the first steps toward an innovative competitive intelligence methodology to study and determine trends within different scientific fields or technologies in society that will enable strategic decisions to be made

From Ignorance to Distrust: The Public “Discovery” of COVID-19 Around International Women’s Day in Spain

In the weeks around March 8, 2020, Spanish political authorities moved from denying and minimizing COVID-19 (veiling international recommendations) to establishing a State of Alarm. This uncertainty scenario is a natural experiment for exploring how concealment and diffusion of critical messages in official discourse affected public and published media, information transmission, and collective risk assessment. This study explores, through Natural Language Processing (NLP) and network theory, press, and Twitter agendas those days when (after international warnings, chaos on data, and the authorization of large demonstrations) Spain made the “alarming discovery” of COVID-19. Results show a swift change in the climate of opinion, from the week before to the week after Women’s Day (March 8). Noninformation influenced agendas in terms of themes, feelings, and behaviors. The way different societies made COVID-19’s “discovery” became essential on the framing of the crisis and on the subsequent trust in authorities during the pandemic. The suppression of information in the first moments remains a key study question

Identificação de fatores que levam à escolha da realização de turismo rural em Portugal utilizando técnicas de Text Mining na plataforma Booking.com

Este estudo pretende descobrir e analisar os fatores mais valorizados no turismo rural, tendo em conta os aspetos positivos e negativos inerentes à estadia em estabelecimentos deste sector turístico. Apresenta-se uma análise de uma amostra com 14.976 comentários obtidos da plataforma de reservas Booking.com sobre estadias em estabelecimentos de turismo rural localizados em Portugal entre 2018 e 2021. Usando técnicas de mineração de texto e modelação por tópicos com o objetivo de encontrar os fatores mais valorizados, obtiveram-se nuvens de palavras que permitem uma análise rápida e intuitiva aos dados. O algoritmo Latent Dirichlet Allocation permitiu extrair dez tópicos coerentes com as nuvens de palavras. Os resultados apontam para que o pequeno-almoço, a simpatia do pessoal, os anfitriões e o espaço exterior sejam os fatores mais relevantes para os hóspedes quando falamos em aspetos positivos. Em termos de aspetos negativos, o pequenoalmoço, limpeza da casa de banho, dificuldades de acesso, rede e WiFi foram os mais relevantes para o estudo. Concluiu-se também que os tópicos encontrados dizem, em grande parte, respeito a fatores controláveis pelo proprietário do estabelecimento. Foi também analisada a coerência, prevalência e relação entre tópicos para os fatores mais valorizados positivamente pelos utilizadores. Estes resultados têm implicação para a gestão das unidades de turismo rural pois identificam os fatores a ter em atenção pelos responsáveis destas unidades

Paratextual Battlegrounds and Critical Power Struggles: Justice League, Black Panther, and Contemporary Film Reception

This explorative work shall consider two blockbuster case studies in Justice League (Snyder, 2017) and Black Panther (Coogler, 2018), assessing how contemporary film reception aids our understanding of power dynamics in the era of social media. I develop ideas of how paratexts work in a contemporary online world, complicating Jonathan Gray’s description of ‘thresholds’ (2010) and introducing the term ‘paratextual battlegrounds’ to depict paratexts as sites of contestation, where the meaning of a text is struggled over. The power of contemporary participatory culture (Jenkins, 2006) has shown traditional hierarchies of power are becoming increasingly vulnerable to challenge from outsider sources and will lead to a reassessment of Pierre Bourdieu’s field theory (1993) that acknowledges this shift. By considering three case studies in contemporary film criticism, I show that we must revisit and revise field theory to accommodate the multiple positions contemporary critics can assume, while also theorising the role of aggregator sites such as Rotten Tomatoes as para-paratexts (Hills, 2015a) sparking producer and fan anxieties. This will ultimately reveal that a contained or co-opted reconfiguration of power has occurred in contemporary social media which invites us to explore the tensions between traditional industry figures and the new possibilities enabled by online sites to complicate dominant positionings and paratextual framings of blockbuster franchises as well as specific films