Misinformation constitutes one of the main challenges to counter the infodemic: misleading news, even if not blatantly false, can cause harm especially in crisis scenarios such as the pandemic. Due to the fast proliferation of information across digital media, human fact-checkers struggle to keep up with fake news, while automatic fact-checkers are not able to identify the grey area of misinformation. We, thus, propose to reverse engineer the manipulation of information offering citizens the means to become their own fact-checkers through digital literacy and critical thinking. Through a corpus analysis of fact-checked news about COVID-19, we identify 10 fallacies–arguments which seem valid but are not–that systematically trigger misinformation and offer a systematic procedure to identify them. Next to fallacies, we examine the types of sources associated to (mis-/dis-)information in our dataset as well as the type of claims making up the headlines. The statistical patterns surfaced from these three levels of analysis reveal a misinformation ecosystem where no source type is exempt from flawed arguments with frequent evading the burden of proof and cherry picking behaviors, even when descriptive claims are at stake. In such a scenario, exercising the audience’s critical skills through fallacy and semantic analysis is necessary to guarantee fake news immunity.
Abstract Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease. Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease. In this paper, we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease. We utilise not only sentiment extracted from tweets, but also the volume of tweets. We present results from experiments exploring the relation between sentiment and future price at different temporal granularities, with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change. Two different neural network models are explored and evaluated, one based on recurrent nets and one based on convolutional networks. An additional model is presented to predict the magnitude of change, which is framed as a multi-class classification problem. It is shown that this model yields more reliable predictions when used alongside a price trend prediction model. The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy ( 63%).
Sentiment Analysis is a technique to determine the tone of a statement using computer software. It is an appliance of a linguistic and computer science intersection that could make a great impact on the business field. Lately, Sentiment Analysis has been used on social media platforms such as Twitter or Facebook to observe the tone of a statement. Examining the tone of tweets using the computer could be time-efficient and precise. SA studies could also be linked to Bitcoin. In this paper, I am using SA results of tweets on a given day to predict changes in the Bitcoin price and its returns. First, I collected the data, which included 31 data points of average sentiment scores and the corresponding 31 Bitcoin prices on the same day. The average sentiment scores were evaluated by VADER from a scale of -1 to 1 (-1 being the statements with the most negative tone, and 1 with the most positive). Then, I used linear regressions to predict Bitcoin price and returns using sentiment scores on the previous day/days. Predicting returns based on sentiments could allow me to find the relationship between Twitter users and Bitcoin and help me better understand the potential challenges. In the end, the predicted price was positively correlated to the sentiment scores the day before. Interestingly, the predicted return was negatively correlated with the sentiment scores and showed less correlation with a M coefficient of -0.86125.
Because of the rising popularity of cryptocurrency in the world, it is essential in these times to understand the market sentiment to make predictions of price and make investment related decisions. Therefore, a model is designed to classify YouTube comments based on cryptocurrency. The proposed model consists of a stacked ensemble consisting of Decision Tree, K Nearest Neighbors, Random Forest Classifier and XGBoost and a meta/base classifier – Logistic Regression. The proposed model achieves an accuracy of 94.2%. In addition, based on our research, we've come to several important findings and takeaways about the current state of cryptocurrencies around the world.
In the modern financial system, the ability to create money is in the hands of a few central institutions. Blockchain networks, and by extension cryptocurrencies, were created with the promise of giving that power to users. The most well-known example of a blockchain technology achieving such decentralization is Bitcoin, but its popularity has arguably been matched by an alternative-currency named Dogecoin. Unlike other cryptocurrencies, which have marketed themselves on differentiating technical features, Dogecoin’s allure likely stems from its cultural roots as a meme. Where cryptocurrency is typically regarded as a difficult topic to grasp, the introduction of Doge’s (2013) most popular meme, into the crypto-space increased crypto’s accessibility to new participants. Consequently, Dogecoin exists in two economies: the financial economy and the cultural meme economy, with the latter having unprecedented tangible impacts on the former. Dogecoin’s unique cultural significance provides an example of how blockchain can succeed in promoting alternative money systems. At its peak in 2021, Dogecoin achieved a market capitalization of $88 billion. Where analysis of the Dogecoin phenomenon is lacking in the current literature, we will fill that gap with a case study of Dogecoin. By studying Dogecoin as a combination of money and meme, we can further our understanding of how to better promote social finance initiatives through the virality of memes.
Eric Edgari, Jocelyn Thiojaya, Nunung Nurul Qomariyah
Bitcoin have become safe-haven for people who want to invest during COVID-19 with its volatile price. Numerous factors can affect the price but recently the most popular one was due to an Elon Musk tweet. We decided to investigate our questions. Do tweets regarding Bitcoin affect its price? Can we predict Bitcoin price by analysing sentiments from twitter? For our research, we decided to analyze the impact of twitter sentiments on Bitcoin price during the COVID-19 pandemic. Using VADER sentiment analysis, we attempted to find out what is the current public sentiment regarding Bitcoin. Coupling tweet sentiment with the Bitcoin price, we pursue making a predictive model to forecast whether Bitcoin price will rise or fall. We also compare whether having twitter sentiment analysis in our model will have an advantage compared to not using. In the end, we found out that twitter sentiment analysis have an impact to Bitcoin price. We hope that our research can help people during this financial stress period.
Purpose The purpose of this paper is to explore the effect of Elon Musk’s Twitter bio change on January 29, 2021 on the discourse around Bitcoin (BTC) on Twitter and to understand how these changes relate to the changes in Bitcoin price around that time. Design/methodology/approach This study implements sentiment analysis and text mining on Twitter data to explore changes in public sentiments toward Bitcoin after Elon Musk’s Twitter bio change. Furthermore, it uses Bitcoin price data obtained from the Binance exchange to understand its relation with Twitter discussion. Findings Elon Musk’s bio change on Twitter on January 29 increased the tweet volume mentioning Bitcoin. This increase in tweets had a strong positive correlation with Bitcoin price and preceded the rise in Bitcoin price. Although the bio change had an apparent effect on the tweet volume, there was no considerable effect on the tweet sentiments, indicating that tweet sentiment is a poor predictor of Bitcoin price. Originality/value This paper proposes an understanding of how social media influencers, like Elon Musk, affect the discourse around Bitcoin and can, in turn, have an impact on Bitcoin price.
Bitcoin has appeared as the highest fortunate cryptocurrency from the last few years. The social network platform Twitter has become a beneficial point of user sentiment. This chapter mainly focuses on the integration of sentiment analysis to predict the response about bitcoin in social media, i.e., Twitter. Through different tweets in Twitter, we have computed the sentiment score. In this chapter, we have taken a survey for knowing what people think about bitcoin from their tweets in Twitter from a single month of both the year, i.e., 2018 and 2019 and compared those. Our result seems to confirm that the trend of Bitcoin price may be predicted from the sentiment without incorporation of complex business models.
Jan 1, 2022·Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China
Bitcoin movement prediction has been a research topic, especially with the Bitcoin bubble period during the COVID-10 pandemic era. It has been discussed that sentiment factors are influential in the prediction process. In this study, to validate the helpfulness of these emotion-related features, thr
Pavlo Seroyizhko, Zhanel Zhexenova, Muhammad Zohaib Shafiq, Fabio Merizzi, Andrea Galassi, Federico Ruggeri. Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP). 2022.
Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.
In recent years, Bitcoin and other cryptocurrencies have been increasingly considered an investment option for emerging markets. However, its erratic behavior has discouraged some potential investors. To get insights into its behavior and price fluctuation, past studies have discovered the correlation between Twitter sentiments and Bitcoin behavior. Most of them have focused exclusively on their relationships, instead of the Twitter sentiment analysis itself. Finding the most suitable classification algorithms for sentiment analysis for this kind of data is challenging. For enormous data of Twitter, unlabeled data can be time-consuming and expensive for the supervised sentiment analysis approach, which has been studied to be superior to unsupervised ones. As such, we propose HyVADRF: Hybrid VADER – Random Forest and Grey Wolf Optimizer Model. Semantic and rule-based VADER was used to calculate polarity scores and classify sentiments, which overcame the weakness of manual labeling, while Random Forest was utilized as its supervised classifier. Furthermore, considering Twitter’s massive size, we collected over 3.6 million tweets and analyzed various dataset sizes as these are related to the model’s learning process. Lastly, Grey Wolf Optimizer parameter tuning was conducted to optimize the classifier’s performance. The results show that 1) HyVADRF Model returned an accuracy of 75.29 %, precision of 70.22%, recall of 87.70%, and F1-score of 78%. 2) The most ideal percentage of dataset size is 90% of the total collected tweets (n=1,249,060). 3) With standard deviations of 0.0008 for accuracy and F1-score and 0.0011 for precision and recall. Hence, HyVADRF Model consistently delivers stable results.
Naila Aslam, Furqan Rustam, Ernesto Lee, Patrick Bernard Washington · 5 authors
The cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate and unique transactions by utilizing cryptographic methods, this industry is still in its inception and serious concerns have been raised about its use. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples’ perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the cryptocurrency which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications including long short term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. Furthermore, TextBlob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used. The proposed LSTM-GRU ensemble shows an accuracy of 0.99 for sentiment analysis, and 0.92 for emotion prediction and outperforms both machine learning and state-of-the-art models.
E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.
Kripto para birimleri 2009 yılında ilk Bitcoin'in ortaya çıkışından bu yana finansal sistemin önemli bir parçası haline gelmiştir. Özellikle de son zamanlarda finansal sistem içerisinde potansiyel değişiklikler meydana getirerek, toplumsal karşılığı ve gelecekteki beklentileri hakkında daha çok gündemi meşgul etmeye başlamıştır. Bu gündem sosyal medya sitelerinde daha çok görülmektedir. Bu çalışmada da Twitter’da #Bitcoin olarak atılan Tweetlerin duygu analizi incelenmiştir. Bunun için Orange Data Mining programı kullanılmıştır. Sonuç olarak; Bitcoin konusunda baskın bir sevinç duygusunun olduğu ve yatırımcıların Bitcoin aldıklarında kendilerini mutlu hissettikleri görülmüştür.
With the aim of bitcoin mining recognition, a method based on community detection is built with small sample of Sichuan daily bitcoin mining electricity consumption data. The method precisely categorizes Sichuan bitcoin mining entities according to their electricity consumption patterns. It is found that Sichuan bitcoin mining activity is strongly featured with seasonality and highly correlated with local hydropower generation. Such a discovery is a key not only to explaining the characteristics of the electricity consumption curves, but also to extending the method summarized from Sichuan case to other provinces. In practice, from Xinjiang power-intensive consumers, suspicious bitcoin miners are detected with the method and their mining activity is found to be correlated with local wind power generation. It is inferred that difficulty in green power utilization during the abundant period results in low electricity price which attracts bitcoin mining activity. By comparing the electricity consumption curves of Sichuan and Xinjiang (suspicious) bitcoin mining entities, a character of peak-valley complementation in temporal distribution implies a nomad electricity consumption of the miners, which could be influential to green power utilization as well as power transmission and distribution.
Muhammad Shahzad, Laiba Bukhari, Tayyeba Muhammad Khan, S. M. Riazul Islam · 6 authors
Natural Language Processing (NLP) is a challenging and evolving field with the potential of mushroom growth. This technology is expected to assume a pivotal role in bridging the gap between human communication and digital data. In line with that, NLP-driven sentiment analysis has become an attractive research area. On the other hand, bitcoin, arguably the most valuable cryptocurrency, has gained popularity as a major source of investment. In this paper, we propose a framework to perform sentiment analysis on Twitter data. We outline the method and results of predicting bitcoin price for a few days in the future. The framework is expected to be helpful in making informed decisions about our investments and policy based on anticipated future trends.
Bitcoin je decentralizirana digitalna valuta, koju se može slati od korisnika do korisnika, bez potrebe za posrednikom. Ovaj rad se bavi predviđanjem cijene valute Bitcoin temeljene na analizi Twittera. Fokusira se na obradu prirodnog jezika, ekstrakciju značajki, treniranje različitih modela strojnog učenja te evaluaciju rezultata. Rezultati su uspoređeni s osnovnim modelom temeljenim samo na cjenovnim indikatorima te su lošiji od njega.
Bütün sektörler dahilinde finans sektöründe de müşterilere ait fikir ve düşüncelerinin belirlenmesi, firma ve kurumların ileriki dönemler için sunacağı hizmetleri etkilemektedir. Kripto para birimlerinin (Bitcoin, Ethereum, Ripple vb.) ekonomik ve sosyal etkileri hızla artmaya devam ettikçe, ilgili haber makalelerinin ve sosyal medya yayınlarının, özellikle de tweetlerin yaygınlığı da artmaktadır. Bu çalışmada, Twitter kullanıcılarının finans sektörü konularından biri olan Bitcoin ile ilgili yorumları derlenerek bir duygu analizi çalışması yapılmıştır. Kullanıcı yorumları, Twitter’ın sunmuş olduğu API hizmeti vasıtasıyla Python Programlama Dili kullanılarak alınmış; yorumlar olumlu, nötr ve olumsuz etiketler ile ayrıştırılmış, etiket bulutunda toplanmıştır. Naïve Bayes ve Lojistik Regresyon algoritmaları kullanılarak oluşturulan modellerde başarı oranları karşılaştırılmıştır. Naïve Bayes uygulamasının tweetlerin duygularını tahmin etmedeki başarı oranı %72,19 olurken, Lojistik Regresyon uygulamasında bu oran %75,53 olmuştur. Çalışmanın ikinci aşamasında ise, duygu analizinden sonra “Bitcoin” anahtar kelimesi içeren günlük pozitif tweet oranı ile Bitcoin günlük açılış değeri beraber kullanılarak Bitcoin kapanış değeri tahminlemesi yapılmıştır. Finans verileri Yahoo Finance web sitesi üzerinden alınmış; Doğrusal Regresyon ve Rastgele Orman Regresyon yöntemleri ile modeller oluşturulmuştur. Doğrusal Regresyon için r² değeri %88,97 çıkarken, Rastgele Orman Regresyonu için ise %94,16 olmuştur.Anahtar Kelimeler: Duygu analizi, Twitter, Bitcoin, Makine öğrenmesi, Veri madenciliği, Finans