Every second a large amount of news is exchanged amongst people with Internet as its driving force. Cheap and easily accessible Internet services across the world have made it even easier for the fake news to spread quickly than the real ones. Moreover, in order to gain TRP (Television Rating Point), many of the news agencies and media houses themselves indulge in malpractices, contributing towards the spread of false news. This sometimes results in riots and political as well as communal instability. Thus, in order to stop the spread of false news, blockchain technology integrated with artificial intelligence and machine learning techniques can be used. In this paper, we have proposed a model based on the above stated technologies named as Reliable News Sharing Platform (RNSP) that aims at ensuring that only real news is communicated and false news is not only detected but is also stopped from being communicated. Anonymous news publication, no central governance, no external interference, credit system are some of the salient features of our proposed model.
Modern day businesses are largely dependent on digital technologies. People prefer viewing the reviews before making any decisions. It applies to all consumables like buying Electronic items, Clothing, Travel, Guest-House, Restaurant, Rental, Housing, Automobile, Cosmetics, Jewellery, Movies, etc. Online services like Mantra, Yelp, Amazon, Facebook, Google My Business, Trip Advisor offer great services to the customer. However, drawbacks of these systems are fake reviews, negative reviews and sometimes even tampering of the reviews given by the customers, which has a huge impact on the business leading to huge financial losses. Sometimes a competitor in the business might also influence the ratings being provided. The centralized storage of these reviews also leads to problems like tampering or manipulation of the data being stored. In this paper we propose an application in the restaurant industry that solves all these drawbacks by making use of the Ethereum blockchain. The food reviews given by the customers are stored as smart contracts in the blockchain, which can't be altered, thus guaranteeing the authenticity of the reviews. Validity of the reviews is ensured because it is difficult for the restaurants to delete or create new accounts to wipe away the bad reviews given. Blockchain is immutable so we ensure that the reviews are genuine and the system is trustable.
Data collected from social media such as tweets, posts, and blogs can assist in an early indication of market sentiment in the financial field. This has frequently been conducted on Twitter data in particular. Using data mining techniques, opinion mining, machine learning, natural language processing (NLP), and knowledge management, the underlying public mood states and sentiment can be uncovered. As cryptocurrencies play an increasingly significant role in global economies, there is an evident relationship between Twitter sentiment and future price fluctuations in Bitcoin. This paper assesses Tweets' collection, manipulation, and interpretation to predict early market movements of cryptocurrency. More specifically, sentiment analysis and text mining methods, including Logistic Regressions, Binary Classified Vector Prediction, Support Vector Mechanism, and Naive Bayes, were considered. Each model was evaluated on their ability to predict public mood states as measured by `tweets' from Twitter during the era of covid-19. An XGBoost-Composite ensemble model is constructed, which achieved higher performance than the state-of-the-art prediction models.
Entertainment industry is growing at a very rapid pace where a huge amount of money is being put into the making of the films. The success or failure of a movie is determined by the box office collection. The box office collection is dependent on various factors like director, actors, actresses, technicians, production house, musicians, marketing, etc. But yet it is highly influenced by the reviews and feedbacks given by the people, critics, media, etc. Due to the latest trends in the marketing field, digital media is used as a form of recommendation system where users read the reviews about a movie, or the ratings rated before making any decision to watch a movie. The existing problem that are identified with these reviews is that nowadays bots are being deployed to increase the reviews, and also fake reviews, negative reviews are also a major concern since most of the reviews are given by people without watching the movies. These problems create huge financial loss to the company, the people associated with it and also the movie industry as a whole. So in order to overcome the existing problems we are considering using blockchain technology as the future of entertainment industry. Since blockchain is a distributed network we can use it to store the reviews given by the user which is nontamperable and this in turn can help rate a movie correctly. Because of the genuine reviews stored in blockchain, movie goers can choose the correct film and make a good film successful thereby increasing the box office collection of the movie.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Besides all uncommon events of 2020, Bitcoin finally passed 20,000 USD in this year, and grabbed more attention about what is going on cryptocurrency and what will happen next? So far, several researchers used social media data in their works and the role of public opinion especially in the specialized forums on Bitcoin price was proved.
With 56 million people actively trading and investing in cryptocurrency online and globally in 2020, there is an increasing need for automatic social media analysis tools to help understand trading discourse and behavior. In this work, we present a dual natural language modeling pipeline which leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. This pipeline first predicts if tweets can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a profit. Next, tweets are input to an unsupervised deep clustering approach to automatically detect trading framing patterns. Our contributions include the modeling pipeline for this novel task, a new Cryptocurrency Tweets Dataset compiled from influential accounts, and a Historical Price Dataset. Our experiments show that our approach achieves an 88.78% accuracy for day trading behavior prediction and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions. 1 Introduction Beginning with the 2008 introduction of Bitcoin (BTC) (Nakamoto, 2008), a cryptocurrency for a Peer-to-Peer cash system, the use of cryptocurrencies and their corresponding blockchains have increasingly gained in popularity. In 2019, the number of Americans owning cryptocurrency doubled from 7% in 2018 to 14%, representing about 35 million people trading and investing with cryptocurrency (Partz, 2019). This increase is largely due to the capability of cryptocurrency to improve various applications ranging from increased security of smart contracts to facilitating less expensive, faster cross-border international payments. Another contributing factor to this growth is that digital coins fulfill the prop-042 erty of storing value similar to other fiat currencies, 043 which are government-issued currencies not backed 044 by physical commodities, e.g., the American dollar 045 or euro. Finally, cryptocurrency popularity can be 046 associated with its high day trading volume. As of 047 January 2021, the combined worth of all cryptocur-048 rencies was $1 trillion 1 , with Bitcoin accounting 049 for $650 billion of this amount. To put this in per-050 spective, the average trading volume of Amazon 051 Inc. is $13 billion per day -less than one-fifth of 052 the BTC daily volume of $70 billion. 2 053 Cryptocurrencies were born on the internet, 054 gained their visibility through online and social 055 media coverage, and many investors follow the 056 advice of well-known cryptocurrency experts on 057 Twitter to guide their personal investment strate-058 gies (Mone, 2019). Because cryptocurrency prices 059 can fluctuate quickly, resulting in real-life financial 060 gains or losses, models that can rapidly analyze 061 trending discourse on Twitter can be harnessed to 062 guide and benefit investors. 063 Additionally, work in computational linguistics 064 and the social sciences have shown the benefit of 065 studying framing, which is how someone discusses 066 a topic in order to influence or alter the opinion of 067 the public, for understanding microblog discourse 068
Social media has become a popular communication platform and aggregated mass information for sentimental analysis. As cryptocurrency has become a hot topic worldwide in recent years, this chapter explores individuals' behavior in sharing Bitcoin information. First, Python was used for extracting around one month's set of Tweet data to obtain a dataset of 11,674 comments during a month of a substantial increase in Bitcoin price. The dataset was cleansed and analyzed by the process documents operator of RapidMiner. A word-cloud visualization for the Tweet dataset was generated. Next, the clustering operator of RapidMiner was used to analyze the similarity of words and the underlying meaning of the comments in different clusters. The clustering results show 85% positive comments on investment and 15% negative ones to Bitcoin-related tweets concerning security. The results represent the generally bullish environment of the cryptocurrency market and general user satisfaction during the period concerned.
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.
Blockchain technology and, in particular, blockchain-based transaction offers us information that has never been seen before in the financial world. In contrast to fiat currencies, transactions through virtual currencies like Bitcoin are completely public. And these transactions of cryptocurrencies are permanently recorded on Blockchain and are available at any time. Therefore, this allows us to build transaction networks (TN) to analyze illegal phenomenons such as phishing scams in blockchain from a network perspective. In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum. Firstly we extract transaction subgraphs for each address and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts. Moreover, Directed-TSGNs, by introducing direction attributes, can retain the transaction flow information that captures the significant topological pattern of phishing scams. By comparing with the TSGN, Directed-TSGN indeed has much lower time complexity, benefiting the graph representation learning. Experimental results demonstrate that, combined with network representation algorithms, the TSGN model can capture more features to enhance the classification algorithm and improve phishing nodes' identification accuracy in the Ethereum networks.
During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.
Social networks such as Twitter contain billions of data of users, and in every second, a large number of tweets trade through Twitter. Sentiment analysis is the way toward deciding the emotional tone behind a series of words that users utilize to understand the attitudes, thoughts, and emotions that are enunciated in online references on Twitter. This chapter aims to determine the user preference of Bitcoin and Ethereum, which are the two most popular cryptocurrencies in the world by using the Twitter sentiment analysis. It proposes a powerful and fundamental approach to identify emotions on Twitter by considering the tweets of these two distinctive cryptocurrencies. One hundred twenty thousand (120,000) tweets were extracted separately from Twitter for each keyword Bitcoin/BTC and Bitcoin/ETC between the period from 12/09/2018 to 22/09/2018 (10 days).
Introduced in 2009, Bitcoin has demonstrated a huge potential as the world’s first digital currency and has been widely used as a financial investment. Our research aims to uncover the relationship between Bitcoin prices and people’s sentiments about Bitcoin on social media. Among various social media platforms, micro-blogging is one of the most popular. Millions of people use micro-blogging platforms to exchange ideas, broadcast views, and to provide opinions on different topics related to politics, culture, science, and technology. This makes them a potentially rich source of data for sentiment analysis. Therefore we chose one of the busiest micro-blogging platforms, Twitter, to perform sentiment analysis on Bitcoin. We used ELMo embedding model to convert Bitcoin-related tweets into a vector form and SVM classifier to divide the tweets into three sentiment categories - positive, negative, and neutral. We then used the sentiment data to find its relation with Bitcoin price fluctuation using the linear mixed model.
Kristina Kapanova, Barbara Guidi, Andrea Michienzi, Kevin Koidl
Online Social Networking platforms (OSNs) are part of the people's everyday life answering the deep-rooted need for communication among humans. During recent years, a new generation of social media based on blockchain became very popular, bringing the power of the technology to the service of social networks. Steemit is one such and employs the blockchain to implement a rewarding mechanism, adding a new, economic, layer to the social media service. The reward mechanism grants virtual tokens to the users capable of engaging other users on the platform, which can be either vested in the platform for increased influence or exchanged for fiat currency. The introduction of an economic layer on a social networking platform can seriously influence how people socialize. In this work, we tackle the problem of understanding how this new business model conditions the way people create contents. We performed term frequency and topic modelling analyses over the written contents published on the platforms between 2017 and 2019. This analysis lets us understand the most common topics of the contents that appear in the platform. While personal mundane information still appears, along with contents related to arts, food, travels, and sport, we also see emerging a very strong presence of contents about blockchain, cryptocurrency and, more specifically, on Steemit itself and its users.
The coronavirus disease 2019 (COVID-19) outbreak has created a great challenge for many industries, including healthcare systems, E-commerce, and transportation. Cryptocurrencies, such as Bitcoin (BTC), is an alternative class of digital assets primarily used as a medium of exchange. In the financial market, data collected from social media such as tweets, posts, and can assist in an early indication of market sentiment. This has frequently been conducted on Twitter data. In particular, this paper provides a corpus of tweet text for Bitcoin-related tweets during the summer of the COVID-19 era. This dataset is publicly available covering a considerable period of roughly three months, to allow people with a similar interest in Twitter, Bitcoin, sentiment analysis, and the financial sector to perform research unimpeded.
Son yıllarda, bloglar, tweet’ler, forumlar, e-postalar gibi Web 2.0 hizmetleri iletişim kanalı olarak yaygın bir şekilde kullanılmaktadır. Ayrıca sosyal medya; gerek bilgi paylaşımı gerekse istek, şikayet ve dilekler gibi görüşleri belirtmenin en kolay ve en güncel yolu olarak düşünülmektedir. Sosyal medyanın, birçok alana olduğu gibi Bitcoin fiyatlarına olan etkisi de son yıllarda tartışılmaktadır. Bitcoin yıllardır üzerinde durulan ve popülerliği her geçen gün artan bir yatırım aracıdır. Merkezi olmayan bir elektronik para birimi sistemi olan Bitcoin, çok sayıda kullanıcının ilgisini çeken, finansal sistemlerdeki köklü bir değişikliği ifade etmektedir. Bu çalışmada sosyal medyanın, özellikle Twitter kanalından elde edilen tweet’ler bazında, Bitcoin fiyatı ile etkileşimi ortaya konulmuştur. Bunun için 06.10.2018-19.05.2019 tarihleri arasında Twitter kullanıcıları tarafından atılan toplam 2.819.784 tweet üzerinden makine öğrenmesi yöntemlerinden sınıflandırma algoritmaları kullanılarak çeşitli analizler gerçekleştirilmiştir. Bulgular değerlendirildiğinde metin sınıflandırmada %90 ile en yüksek doğruluk oranına sahip olan Yapay Sinir Ağları kullanılmıştır. Ayrıca Bitcoin fiyatları ve sınıflandırılmış olumlu/olumsuz tweet oranları ile ikili korelasyon yapılmıştır. Elde edilen 0,681 korelasyon katsayısı ile pozitif yönde orta üstü kuvvetli ilişki tespit edilmiştir.
Blockchain implementation brought several benefits to many areas. One of the usages of blockchain is in digital currencies. Digital currency (cryptocurrency) is a new era for the global financial system. Cryptocurrencies draw significant attention from researchers because of their advantages. Although there are several risks (e.g., speculation, 51% attack) related to cryptocurrency, billions of dollars are invested in them, because of their transparency, traceability, low transaction cost, and highly profitable potential. In December 2017, the most famous cryptocurrency, Bitcoin, has reached almost $20,000.00 per coin. Such short-term, high gain potential attracts many new small investors. However, speculative movements raise many questions related to the safety and privacy of investors, just to name a few. To understand public opinions about cryptocurrency and speculative movements to protect small investors financial interests, sentiment analysis can be done by using social media activities of individuals who are interested or investing in cryptocurrencies. It is also one of the essential steps in the analysis to understand the profiles of the users. Therefore, in this paper, we determine the attitudes of social network users by analyzing the positivity and negativity of the comments about six cryptocurrencies. Results show that the positivity is higher than negativity, and there exist relations between price changes and attitudes. However, relations vary according to currency types. The results and analysis, which are provided in this paper, help new investors and developers to obtain opinions of social network users who are interested or investing in cryptocurrency.
We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin. Bitcoin is the leading cryptocurrency and has the highest market capitalization among digital currencies. Predicting Bitcoin values may help understand and predict potential market movement and future growth of the technology. Unlike (mostly) repeating phenomena like weather, cryptocurrency values do not follow a repeating pattern and mere past value of Bitcoin does not reveal any secret of future Bitcoin value. Humans follow general sentiments and technical analysis to invest in the market. Hence considering people's sentiment can give a good degree of prediction. We focus on using social sentiment as a feature to predict future Bitcoin value, and in particular, consider Google News and Reddit posts. We find that social sentiment gives a good estimate of how future Bitcoin values may move. We achieve the lowest test RMSE of 434.87 using an LSTM that takes as inputs the historical price of various cryptocurrencies, the sentiment of news articles and the sentiment of Reddit posts.
Abstract The paper proposes the exploration, identification and development of a Java solution for extracting the sentiment related to the cryptocurrencies phenomenon, from the content of the posts of certain popular social networks. Detecting the positive, neutral or negative character of the sentiment is adopted as a relevant method of establishing the nature of the human perception on the topical issue defined by cryptocurrencies.
Purpose Crypto-currencies, decentralized electronic currencies systems, denote a radical change in financial exchange and economy environment. Consequently, it would be attractive for designers and policy-makers in this area to make out what social media users think about them on Twitter. The purpose of this study is to investigate the social opinions about different kinds of crypto-currencies and tune the best-customized classification technique to categorize the tweets based on sentiments. Design/methodology/approach This paper utilized a lexicon-based approach for analyzing the reviews on a wide range of crypto-currencies over Twitter data to measure positive, negative or neutral sentiments; in addition, the end result of sentiments played a training role to train a supervised technique, which can predict the sentiment loading of tweets about the main crypto-currencies. Findings The findings further prove that more than 50 per cent of people have positive beliefs about crypto-currencies. Furthermore, this paper confirms that marketers can predict the sentiment of tweets about these crypto-currencies with high accuracy if they use appropriate classification techniques like support vector machine (SVM). Practical implications Considering the growing interest in crypto-currencies (Bitcoin, Cardano, Ethereum, Litcoin and Ripple), the findings of this paper have a remarkable value for enterprises in the financial area to obtain the promised benefits of social media analysis at work. In addition, this paper helps crypto-currencies vendors analyze public opinion in social media platforms. In this sense, the current paper strengthens our understanding of what happens in social media for crypto-currencies. Originality/value For managers and decision-makers, this paper suggests that the news and campaign for their crypto in Twitter would affect people’s perspectives in a good manner. Because of this fact, the firms, investing in these crypto-currencies, could apply the social media as a magnifier for their promotional activities. The findings steer the market managers to see social media as a predictor tool, which can analyze the market through understanding the opinions of users of Twitter.
We develop a new Data-Driven Phasic Word Identification (DDPWI) methodology to determine which words matter as the bitcoin pricing dynamic changes from one phase to another. With Google search volumes as a baseline, we find that Reddit submissions are both correlated with Google and have a comparable relationship with a variety of bitcoin metrics, using Spearman's rho. Reddit provides complete access to the text of submissions. Rather than associating sentiment with market activity, we describe the DDPWI method for finding specific 'price dynamic' words associated with changes in the bitcoin pricing pattern through 2017 and 2018. We assess the significance of these changes using Wilcoxon Rank-Sum Tests with Bonferroni corrections. These price dynamic words are used to pull out associated words in the submissions thereby providing the context to their use. For example, the price dynamic word 'ban', which became significantly higher in frequency as prices fell, occurred in the context of both government regulation and internet companies banning cryptocurrency adverts. This approach could be used more generally to look at social media and discussion forums at a granular level identifying specific words that impact the metric under investigation rather than overall sentiment.
Aditi Mittal, Vipasha Dhiman, Ashi Singh, Chandra Prakash
In recent years, the social network has been widely used among the public to share their views and for communication as well. Further, sentiments of the text used in emails, blogs, and social media posts affect human decision making and behavior. Bitcoin being a decentralized and peer-to-peer cryptocurrency has attracted a large number of users on the web search and social media. The goal of this paper is to correlation among Bitcoin price and Twitter and Google search patterns. Linear regression, polynomial regression, Recurrent Neural Network, and Long Short Term Memory based analysis concludes that there is a relevant degree of correlation of Google Trends and Tweet volume data with the price of Bitcoin, and with no significant relation with the sentiments of tweets.