Cryptocurrencies represent one of the most attractive markets for financial speculation. As a consequence, they have attracted unprecedented attention on social media. Besides genuine discussions and legitimate investment initiatives, several deceptive activities have flourished. In this work, we chart the online cryptocurrency landscape across multiple platforms. To reach our goal, we collected a large dataset, composed of more than 50M messages published by almost 7M users on Twitter, Telegram and Discord, over three months. We performed bot detection on Twitter accounts sharing invite links to Telegram and Discord channels, and we discovered that more than 56% of them were bots or suspended accounts. Then, we applied topic modeling techniques to Telegram and Discord messages, unveiling two different deception schemes - “pump-and-dump” and “Ponzi” - and identifying the channels involved in these frauds. Whereas on Discord we found a negligible level of deception, on Telegram we retrieved 296 channels involved in pump-and-dump and 432 involved in Ponzi schemes, accounting for a striking 20% of the total. Moreover, we observed that 93% of the invite links shared by Twitter bots point to Telegram pump-and-dump channels, shedding light on a little-known social bot activity. Charting the landscape of online cryptocurrency manipulation can inform actionable policies to fight such abuse.
Many chatbots have been developed that provide a multitude of services through a wide range of methods. A chatbot is a brand-new conversational agent in the highspeed changing technology world. With the advance of Artificial Intelligence and machine learning, chatbots are becoming more and more popular. A chatbot is the extension of human interface mediums such as the phone and social platforms. Similarly, Cryptocurrency is a new extension of digital or virtual currency designed to work as a medium of exchange. In the current digital exchanging world, investors and interested parties are eager to know more information about, and the capabilites of, this new type of currency. One of the potential paths to retrieve the info automatically and quickly is through a chatbot. We explored the open source python library, Chatterbot, to apply Itchat API (a WeChat interface) with the aim of building a robot chatting application, I&C Chat, on the topic of cryptocurrency. First, we collected question and answer pairs datasets from Quora websites. Furthermore, we also created API calls to query the real time quote for the top 25 cryptocurrencies. Then we used the collected data to train our chatbot and implemented a logic adapter to receive the price quote of cryptocurrencies based on the incoming question. The Itchat API method will return the best matched answer to the asking party automatically. The response time of different questions has been investigated. The results imply that this application is quite useful, feasible and beneficial to the digital currency world.
Cryptocurrency is one of the burning issues across the world in the modern era. Literature shows that business analysts always tend to use new technologies and investigate their risks. Some researchers predicted the price fluctuations and investigated the risk of cryptocurrency and blockchain network. However, to the best of our knowledge, there is no study which provides the feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The other cryptocurrencies are not included in the study because of having insufficient relevant data on the social forums. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Apart from that, the public perception and environment feasibility are also determined. Almost 83.7% tweets of Sweden shows positive sentiment for cryptocurrency investment. Which ranks the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the significant terms are also determined using Topic modeling from public opinions. Such as authorization rules, investment, profit, volatility, and security etc. These terms are almost identified as subtopic for each of the inquired keyword. This shows the mutual relationship among different cryptocurrencies and public's concerns about cryptocurrencies.
Public interest in cryptocurrencies has consistently risen over the past decade. Owing to this rapid growth, cryptocurrency-related information is being increasingly shared online. As considerable portions of such information in online communities are noise, extracting meaningful information is important. Therefore, judging whose opinion should be considered more important or who the opinion leaders in online communities are is critical. This study analyzed the topics that contain meaningful information, in particular, user groups, by investigating the correlation between topic weights and their price change. The proposed analysis method involves (1) effective classification of the user groups using a hypertext-induced topic selection algorithm, (2) textual information analysis through topic modeling, and (3) the identification of user groups that have a high interest in the Bitcoin price by measuring the correlation between the price and the topics and by measuring the topic similarities between each user group and all users to determine the user group that can effectively represent the entire community. By analyzing the information shared by users, we observed that most users are interested in the price information, whereas users having social influence are not only interested in the price but also in other information.
Shaista Bibi, Shahid Hussain, Muhammad Imran Faisal
Cryptocurrency is one of the emerging online currency of the modern era. Big companies are investing in this technology. However, some established companies still hesitate to use it. According to them, it is a volatile trend which will fade up eventually. There is no such authority which will provide them feasibility information. So, investors can be helped by providing them feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The crawled data for other cryptocurrencies are much insufficient, so that are excluded from the study. In the proposed methodology, the top locations where cryptocurrency is widely used are identified, then in that particular locations' users concerns along with their sentiment analyses are investigated. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Almost 83.7% tweets of Sweden show positive sentiment for cryptocurrency investment which ranks as the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the noteworthy terms found are legitimacy, authorization rules, volatility, profit, investment, and fluctuations. Which describe the users' concerns/ interests' about cryptocurrency. Investors can focus on all these areas during business. These subtopics can help business experts to evolve their businesses' and to make them more sustainable on the basis of public perception.
Chelsea Hinds-Charles, Jenelee Adames, Ye Yang, Yusong Shen · 5 authors
As one of the most successful Blockchain systems, Bitcoin evolved over the past 8 years. The collaborative contribution of its online software development community gradually shaped the functionality and performance of Bitcoin. To date, most discussions around Bitcoin are from technologies underlying the product, as well as market applications. There are very few studies on the development and evolution processes of the Bitcoin software. It is important to investigate on such developmental issues, in order to better understand the development methodologies and lessons learnt from such a spearheaded Blockchain system. This paper serves this purpose by examining the issues data extracted from the Bitcoin GitHub repository from 2011 to 2018. It reports the trends of the major development issues from a longitude perspective. The main results include: 1) the average lifespan of an issue in Bitcoin issue repository is approximately 57 days; and 2) the Top-7 issue types including refactoring, tests, doc, RPC.REST.ZMQ, GUI, bugs, and wallet, accounting for 64.3% of all issues; 3) topic modeling techniques are beneficial in mining popularity and evolution of key issue topics and most problematic architecture components. Using data analysis and visualization techniques, this paper suggests the insights for significant development decisions such as better managing issue repository and strategic allocating of bug resolution effort.
Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion and relational information retrieval. A simple, yet surprisingly accurate method for representing a relation between two words is to compute the vector offset (\PairDiff) between the corresponding word embeddings. Despite its empirical success, it remains unclear whether \PairDiff is the best operator for obtaining a relational representation from word embeddings. In this paper, we conduct a theoretical analysis of the \PairDiff operator. In particular, we show that for word embeddings where cross-dimensional correlations are zero, \PairDiff is the only bilinear operator that can minimise the $\ell_{2}$ loss between analogous word-pairs. We experimentally show that for word embedding created using a broad range of methods, the cross-dimensional correlations in word embeddings are approximately zero, demonstrating the general applicability of our theoretical result. Moreover, we empirically verify the implications of the proven theoretical result in a series of experiments where we repeatedly discover \PairDiff as the best bilinear operator for representing semantic relations between words in several benchmark datasets.
The block size debate has been a contentious issue in the Bitcoin com-munity on the social media platform Reddit. Many members of the com-munity suspect there have been organized attempts to manipulate the debate from people using multiple accounts to over-represent and mis-represent important issues on the debate. The following analysis uses techniques from authorship attribution and machine learning to deter-mine whether comments from user accounts that are active in the debate are from the same author. The techniques used are able to recall over 90 % of all instances of multiple account use and achieve up to 72 % for the true positive rate. 1
The Information Age has made publishing, distributing and collecting information easier, resulting in the exponential growth of information available to us. Databases were once ledgers written by hand by a single person; today they can be vast stores of data agglomerated from a myriad of disparate sources. The mass media, formerly limited to newspapers and television programs held to strict journalistic standards, has expanded to include collaborative content such as blogs, wikis and message boards. Documents covering Judging of Sources nearly every topic abound on the Internet, but the authors are often anonymous and the accuracy uncertain.
Kyle Richardson, Danny Bobrow, Cleo Condoravdi, Richard Waldinger · 5 authors
We present work on using a domain model to guide text interpretation, in the context of a project that aims to interpret English questions as a sequence of queries to be answered from structured databases. We adapt a broad-coverage and ambiguity-enabled natural language processing (NLP) system to produce domain-specific logical forms, using knowledge of the domain to zero in on the appropriate interpretation. The vocabulary of the logical forms is drawn from a domain theory that constitutes a higher-level abstraction of the contents of a set of related databases. The meanings of the terms are encoded in an axiomatic domain theory. To retrieve information from the databases, the logical forms must be instantiated by values constructed from fields in the database. The axiomatic domain theory is interpreted by the first-order theorem prover SNARK to identify the groundings, and then retrieve the values through procedural attachments semantically linked to the database. SNARK attempts to prove the logical form as a theorem by reasoning over the theory that is linked to the database and returns the exemplars of the proof(s) back to the user as answers to the query. The focus of this paper is more on the language task, however, we discuss the interaction that must occur between linguistic analysis and reasoning for an end-to-end natural language interface to databases. We illustrate the process using examples drawn from an HIV treatment domain, where the underlying databases are records of temporally bound treatments of individual patients.