The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity
Microblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a dec...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/6/2/59 |
| _version_ | 1827662321555603456 |
|---|---|
| author | Maria Trigka Andreas Kanavos Elias Dritsas Gerasimos Vonitsanos Phivos Mylonas |
| author_facet | Maria Trigka Andreas Kanavos Elias Dritsas Gerasimos Vonitsanos Phivos Mylonas |
| author_sort | Maria Trigka |
| collection | DOAJ |
| description | Microblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a decentralized cryptographic currency and is equivalent to most recurrently known currencies in the way that it is influenced by socially developed conclusions, regardless of whether those conclusions are considered valid. This work aims to assess the importance of Twitter users’ profiles in predicting a cryptocurrency’s popularity. More specifically, our analysis focused on the user influence, captured by different Twitter features (such as the number of followers, retweets, lists) and tweet sentiment scores as the main components of measuring popularity. Moreover, the Spearman, Pearson, and Kendall Correlation Coefficients are applied as post-hoc procedures to support hypotheses about the correlation between a user influence and the aforementioned features. Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period. Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting future values of the cryptocurrency popularity. |
| first_indexed | 2024-03-10T00:24:25Z |
| format | Article |
| id | doaj.art-1439b21e0bfe4d6ca9921019cae5cd04 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T00:24:25Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-1439b21e0bfe4d6ca9921019cae5cd042023-11-23T15:36:25ZengMDPI AGBig Data and Cognitive Computing2504-22892022-05-01625910.3390/bdcc6020059The Predictive Power of a Twitter User’s Profile on Cryptocurrency PopularityMaria Trigka0Andreas Kanavos1Elias Dritsas2Gerasimos Vonitsanos3Phivos Mylonas4Computer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceDepartment of Digital Media and Communication, Ionian University, 28100 Kefalonia, GreeceComputer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceComputer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceDepartment of Informatics, Ionian University, 49100 Corfu, GreeceMicroblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a decentralized cryptographic currency and is equivalent to most recurrently known currencies in the way that it is influenced by socially developed conclusions, regardless of whether those conclusions are considered valid. This work aims to assess the importance of Twitter users’ profiles in predicting a cryptocurrency’s popularity. More specifically, our analysis focused on the user influence, captured by different Twitter features (such as the number of followers, retweets, lists) and tweet sentiment scores as the main components of measuring popularity. Moreover, the Spearman, Pearson, and Kendall Correlation Coefficients are applied as post-hoc procedures to support hypotheses about the correlation between a user influence and the aforementioned features. Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period. Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting future values of the cryptocurrency popularity.https://www.mdpi.com/2504-2289/6/2/59blockchaincryptocurrencyKendall Correlation CoefficientPearson Correlation Coefficientsentiment analysissocial media analytics |
| spellingShingle | Maria Trigka Andreas Kanavos Elias Dritsas Gerasimos Vonitsanos Phivos Mylonas The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity Big Data and Cognitive Computing blockchain cryptocurrency Kendall Correlation Coefficient Pearson Correlation Coefficient sentiment analysis social media analytics |
| title | The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity |
| title_full | The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity |
| title_fullStr | The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity |
| title_full_unstemmed | The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity |
| title_short | The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity |
| title_sort | predictive power of a twitter user s profile on cryptocurrency popularity |
| topic | blockchain cryptocurrency Kendall Correlation Coefficient Pearson Correlation Coefficient sentiment analysis social media analytics |
| url | https://www.mdpi.com/2504-2289/6/2/59 |
| work_keys_str_mv | AT mariatrigka thepredictivepowerofatwitterusersprofileoncryptocurrencypopularity AT andreaskanavos thepredictivepowerofatwitterusersprofileoncryptocurrencypopularity AT eliasdritsas thepredictivepowerofatwitterusersprofileoncryptocurrencypopularity AT gerasimosvonitsanos thepredictivepowerofatwitterusersprofileoncryptocurrencypopularity AT phivosmylonas thepredictivepowerofatwitterusersprofileoncryptocurrencypopularity AT mariatrigka predictivepowerofatwitterusersprofileoncryptocurrencypopularity AT andreaskanavos predictivepowerofatwitterusersprofileoncryptocurrencypopularity AT eliasdritsas predictivepowerofatwitterusersprofileoncryptocurrencypopularity AT gerasimosvonitsanos predictivepowerofatwitterusersprofileoncryptocurrencypopularity AT phivosmylonas predictivepowerofatwitterusersprofileoncryptocurrencypopularity |