Social Network Analysis and Mining

Background: Over 450 million people worldwide have a rare disease. Yet despite healthcare policy rhetoric placing an onus on inclusive public engagement, rare disease publics are often engaged as data sources or product/service consumers. Meanwhile, various rare disease actors congregate around ‘Rare Disease Day’ each year – a global event with various online and offline talks, workshops, and sessions. In 2021, ~4.3 million tweets marked Twitter as a locus of exchange for the event. Methods: To examine public discourse around the event, the paper draws on social network and qualitative analyses of 40,366 Twitter tweets/retweets about rare disease day 2021 posted between 10-Feb-2021 and 10-Mar-2021, analysing them through a controversy theory lens. After identifying particularly influential Twitter users and groups, the paper examines their textual and visual communication strategies. Results: It funds three distinct orientations to rare disease discourse on Twitter (mission, awarene...

to the UCM Research Group 940241. Coordinator of the Department of Didactics and School Organisation (DOE) Doctoral Program. Director of Doctoral Seminars. Coordinator and Tutor of the Pedagogy Practicum. Antonio Bartolomé Pina, PhD in Philosophy and Educational Sciences. He is Professor Emeritus at the University of Barcelona where he has been Professor of Digital Media and Education and Director of the Institute for Research in Education. He is author or co-author of 35 books and 183 articles or book chapters. He has given 271 conferences in different countries and directed or participated in some 50 international research projects. His x Contributors two main lines of research have been the design of multimedia learning environments and technology-enhanced learning assessment.

This paper investigates public sentiment on the future impact of artificial intelligence (AI) by analysing Reddit data using lexiconbased sentiment analysers and a machine learning approach. The study utilizes data from a manually selected subreddit to ensure the dataset's relevance while reducing noise to improve sentiment results. Sentiment analysis was conducted using VADER, TextBlob, and BERT, which provided scores indicating negative, neutral, or positive polarity. Data preprocessing involved stop word removal to enhance model performance. Results were visualized using WordCloud and Matplotlib graphs including histograms, bar graphs, and pie charts to compare sentiment distributions across the different analysers. The findings reveal varied public opinions on AI's future, as well as a discrepancy between the models. From 559 comments, VADER reported 23% negative, 22% neutral, and 55% positive sentiments; TextBlob found 16% negative, 37% neutral, and 47% positive sentiments; and BERT indicated 42% negative, 28% neutral, and 30% positive sentiments. Notably, the overall public opinion on AI's future impact was positive, albeit with variances across sentiment analysers. This study highlights the discrepancies between different sentiment analysis tools and underscores the need for further comparative research.

Classification problems have made significant progress due to the maturity of artificial intelligence (AI). However, differentiating items from categories without noticeable boundaries is still a huge challenge for machines -which is also crucial for machines to be intelligent. In order to study the fuzzy concept on classification, we define and propose a globalness detection with the four-stage operational flow. We then demonstrate our framework on Facebook public pages interlike graph with their geo-location. Our prediction algorithm achieves high precision (89%) and recall (88%) of local pages. We evaluate the results on both states and countries level, finding that the global node ratios are relatively high in those states (NY, CA) having large and international cities. Several global nodes examples have also been shown and studied in this paper. It is our hope that our results unveil the perfect value from every classification problem and provide a better understanding of global and local nodes in Online Social Networks (OSNs).

The advancement in computational technology has revolutionized the finance industry by changing the way investors trade stocks, raising new challenges to the decision-making process. The interaction between the finance and technology industries has spurred the development of various artificial intelligence (AI) technologies that transform the analysis of the stock market.

Coordinated online behaviors are an essential part of information and influence operations, as they allow a more effective disinformation's spread. Most studies on coordinated behaviors involved manual investigations, and the few existing computational approaches make bold assumptions or oversimplify the problem to make it tractable. Here, we propose a new network-based framework for uncovering and studying coordinated behaviors on social media. Our research extends existing systems and goes beyond limiting binary classifications of coordinated and uncoordinated behaviors. It allows to expose different coordination patterns and to estimate the degree of coordination that characterizes diverse communities. We apply our framework to a dataset collected during the 2019 UK General Election, detecting and characterizing coordinated communities that participated in the electoral debate. Our work conveys both theoretical and practical implications and provides more nuanced and finegrained results for studying online information manipulation.

Coordinated online behaviors are an essential part of information and influence operations, as they allow a more effective disinformation's spread. Most studies on coordinated behaviors involved manual investigations, and the few existing computational approaches make bold assumptions or oversimplify the problem to make it tractable. Here, we propose a new network-based framework for uncovering and studying coordinated behaviors on social media. Our research extends existing systems and goes beyond limiting binary classifications of coordinated and uncoordinated behaviors. It allows to expose different coordination patterns and to estimate the degree of coordination that characterizes diverse communities. We apply our framework to a dataset collected during the 2019 UK General Election, detecting and characterizing coordinated communities that participated in the electoral debate. Our work conveys both theoretical and practical implications and provides more nuanced and fine-gra...

In this paper, we present overview of our approach for clustering tweets. Due to short text of tweets, traditional text clustering mechanisms alone may not produce optimal results. We believe that there is an underlying theme/topic present in majority of tweets which is evident in growing usage of hashtag feature in the Twitter network. Clustering tweets based on these themes seems a more natural way for grouping. We propose to use Wikipedia topic taxonomy to discover the themes from the tweets and use the themes along with traditional word based similarity metric for clustering. We show some of our initial results to demonstrate the effectiveness of our approach.

Nowadays, social networks are more and more widely used as a solution for enriching users' profiles in systems such as recommender systems or personalized systems. For an unknown user's interest, the user's social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the user's social network and strongly depend on each author's objective. To improve these techniques, we propose using a community-based algorithm that is applied to a part of the user's social network (egocentric network) and that derives a user social profile that can be reused for any purpose (e.g., personalization, recommendation). We compute weighted user's interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g., centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individual-based techniques and the influence of structural measures (related to communities) on the quality of derived profiles. A second experiment on DBLP and the author's social network Mendeley confirms the results obtained on Facebook and shows the influence of the density of egocentrics network on the quality of results.

The widespread availability of Customer Relationship Management applications in modern organizations, allows companies to collect and store vast amounts of high-detailed customer-related data. Making sense of these data using appropriate methods can yield insights into customers' behaviour and preferences. The extracted knowledge can then be explored for marketing purposes. Social Network Analysis techniques can play a key role in business analytics. By modelling the implicit relationships among customers as a social network, it is possible to understand how patterns in these relationships translate into competitive advantages for the company. Additionally, the incorporation of the temporal dimension in such analysis can help detect market trends and changes in customers' preferences. In this paper, we introduce a methodology to examine the dynamics of customer communities, which relies on two dierent time window models: a landmark and a sliding window. Landmark windows keep all the historical data and treat all nodes and links equally, even if they only appear at the early stages of the network life. Such approach is appropriate for the long term analysis of networks, but may fail to provide a realistic picture of the current evolution. On the other hand, sliding

The COVID-19 pandemic has triggered unprecedented global discussions, with social media platforms, particularly Twitter, becoming key channels for information exchange, sentiment expression, and trend formation. This study aims to explore and understand the evolving conversations surrounding COVID-19 by leveraging Twitter data. Using advanced analytics and data visualization techniques, we will decode trends, sentiments, and emerging narratives in tweets related to the pandemic. The study collects large-scale Twitter datasets, applying natural language processing (NLP) to analyze patterns in tweet content. By visualizing the data through interactive dashboards, the study will highlight key trends, such as public sentiment shifts, regional differences, and trending topics over time. This approach enables a comprehensive understanding of how COVID-19 conversations have evolved throughout various phases of the pandemic. The results will provide insights into the key concerns and attitudes expressed by users across different geographical locations and periods, offering valuable perspectives for public health authorities, policymakers, and researchers. By understanding these patterns, the study aims to enhance preparedness and communication strategies for future global health crises. This research contributes to the growing body of knowledge on the role of social media in shaping public discourse during pandemics.

In the stock market, which is a dynamic, complex, nonlinear and non-parametric environment, accurate prediction is crucial for trading strategy. It is assumed that news articles affect the stock market. We investigated the relationship[1] between headline’s sentiment of news and their impact on stock prices changes. To show this relationship, we applied the sentiment data and the price difference between the day before the news was published and the day of the news, to machine learning regression and classification models. Regression is used to predict changes and classification is used to decide whether to buy or sell stocks.  We used three stock datasets named Apple, Amazon and AXP and the results are shown in the mentioned dataset that using news with negative sentiments can make predictions just as correctly as using news with both positive and negative sentiments. In regression and classification models, Random Forest outperformed other machine learning algorithms in predicting stock price changes using news sentiment analysis. Additionally, we depicted that the results of computer and human tagging were almost similar, showing that using computer tools for text tagging will allow to tag text much more quickly and easily.

Following the 2016 US elections Twitter launched their Information Operations (IO) hub where they archive account activity connected to state linked information operations. In June 2020, Twitter took down and released a set of accounts linked to Turkey's ruling political party (AKP). We investigate these accounts in the aftermath of the takedown to explore whether AKP-linked operations are ongoing and to understand the strategies they use to remain resilient to disruption. We collect live accounts that appear to be part of the same network, 30% of which have been suspended by Twitter since our collection. We create a BERT-based classifier that shows similarity between these two networks, develop a taxonomy to categorize these accounts, find direct sequel accounts between the Turkish takedown and the live accounts, and find evidence that Turkish IO actors deliberately construct their network to withstand large-scale shutdown by utilizing explicit and implicit signals of coordination. We compare our findings from the Turkish operation to Russian and Chinese IO on Twitter and find that Turkey's IO utilizes a unique group structure to remain resilient. Our work highlights the fundamental imbalance between IO actors quickly and easily creating free accounts and the social media platforms spending significant resources on detection and removal, and contributes novel findings about Turkish IO on Twitter.

The analysis of large-scale complex networks is a major challenge in the Big Data domain. Given the large-scale of the complex networks researchers commonly deal with nowadays, the use of localized information (i.e. restricted to a limited neighborhood around each node of the network) for centrality-based analysis is gaining momentum in the recent literature. In this context, we propose a framework for the Distributed Assessment of Network Centralities (DANCE) in complex networks. DANCE offers a single environment that allows the use of different localized centrality proposals, which can be tailored to specific applications. This environment can be thus useful given the vast potential applicability of centrality-based analysis on large-scale complex networks found in different areas, such as Biology, Physics, Sociology, or Computer Science. Since the localized centrality proposals DANCE implements employ only localized information, DANCE can easily benefit from parallel processing environments and run on different computing architectures. To illustrate this, we present a parallel implementation of DANCE and show how it can be applied to the analysis of large-scale complex networks using different kinds of network centralities. This implementation is made available to complex network researchers and practitioners interested in using it through a scientific web portal.

Background: Information is essential for growth; without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of the certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons. Aims: This study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the coronavirus pandemic (COVID-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning. Methodology: The hashtag 'COVID-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a Twitter tokenizer, while TextBlob and Valence Aware Dictionary for Sentiment Reasoning (VADER) were used for text mining and sentiment analysis, respectively. Topic modelling was done with Latent Dirichlet Allocation and visualized with Multidimensional scaling. Results: The result of the VADER sentiment returned 39.8%, 31.3%, and 28.9%, positive, neutral, and negative sentiment, respectively, while the result of the TextBlob sentiment returned 46.0%, 36.7%, and 17.3%, neutral, positive, and negative sentiment, respectively. Conclusion: With all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of COVID-19. Also, know the opinion and challenges of people, then deal with the problem of misinformation. It is concluded that with popular belief a significant number of the populace regards COVID-19 as a virus that has come to stay, some believe it will eventually be conquered.

The COVID-19 pandemic has triggered unprecedented global discussions, with social media platforms, particularly Twitter, becoming key channels for information exchange, sentiment expression, and trend formation. This study aims to explore and understand the evolving conversations surrounding COVID-19 by leveraging Twitter data. Using advanced analytics and data visualization techniques, we will decode trends, sentiments, and emerging narratives in tweets related to the pandemic. The study collects large-scale Twitter datasets, applying natural language processing (NLP) to analyze patterns in tweet content. By visualizing the data through interactive dashboards, the study will highlight key trends, such as public sentiment shifts, regional differences, and trending topics over time. This approach enables a comprehensive understanding of how COVID-19 conversations have evolved throughout various phases of the pandemic. The results will provide insights into the key concerns and attitudes expressed by users across different geographical locations and periods, offering valuable perspectives for public health authorities, policymakers, and researchers. By understanding these patterns, the study aims to enhance preparedness and communication strategies for future global health crises. This research contributes to the growing body of knowledge on the role of social media in
shaping public discourse during pandemics.

This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in communication networks, and the connector hubs in biological systems. In this paper, we introduce DICeNod, a new approach to efficiently identify central nodes in social networks without direct measurement of each individual node using compressive sensing, which is a well-known paradigm in sparse signal recovery. DICeNod can perform in a distributed manner by utilizing only local information at each node; thus, it is appropriate for large real-world and unknown networks. We theoretically show that by using only O k log(n k) indirect end-to-end measurements in DICeNod, one can recover top-k central nodes in a network with n nodes, even though the measurements have to follow network topological constraints. Furthermore, we show that the computationally efficient 1-minimization can provide recovery guarantees to infer such central nodes from the constructed measurement matrix with this number of measurements. Finally, we evaluate accuracy, speedup, and effectiveness of the proposed method by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that the number of correctly identified central nodes and their estimated rank based on the DICeNod framework and the global betweenness centrality correlate very well. Moreover, the results show that DICeNod is a scalable framework and it can be efficiently used for identifying central nodes in real-world networks.

This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in communication networks, and the connector hubs in biological systems. In this paper, we introduce DICeNod, a new approach to efficiently identify central nodes in social networks without direct measurement of each individual node using compressive sensing, which is a well-known paradigm in sparse signal recovery. DICeNod can perform in a distributed manner by utilizing only local information at each node; thus, it is appropriate for large real-world and unknown networks. We theoretically show that by using only O k log(n k) indirect end-to-end measurements in DICeNod, one can recover top-k central nodes in a network with n nodes, even though the measurements have to follow network topological constraints. Furthermore, we show that the computationally efficient 1-minimization can provide recovery guarantees to infer such central nodes from the constructed measurement matrix with this number of measurements. Finally, we evaluate accuracy, speedup, and effectiveness of the proposed method by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that the number of correctly identified central nodes and their estimated rank based on the DICeNod framework and the global betweenness centrality correlate very well. Moreover, the results show that DICeNod is a scalable framework and it can be efficiently used for identifying central nodes in real-world networks.

Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node uses local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.

The emergence of social media has provided a platform for individuals to express a wide range of sentiments and emotions. Sentiment analysis, the task of determining the emotional tone behind text data, has gained prominence for its relevance in various domains. This research project aims to address a notable gap in existing sentiment analysis systems by focusing on sentiments that are often overlooked, including depression, suicidal thoughts, feelings of threat, fear, and other emotionally charged states. The project utilizes fine-tuned language models to achieve more accurate and comprehensive sentiment analysis in the context of social media. Through data collection, preprocessing, fine-tuning, and evaluation, the research contributes to the improvement of sentiment analysis for a broader range of emotions and states expressed on social media.

This research investigates the dynamics of COVID-19 misinformation spread on Twitter within the unique context of Finland. Employing cutting-edge methodologies including text classification, topic modeling, social network analysis, and correspondence analysis (CA), the study analyzes 1.6 million Finnish tweets from December 2019 to October 2022. Misinformation tweets are identified through text classification and grouped into topics using BERTopic modeling. Applying the Leiden algorithm, the analysis uncovers retweet and mention networks, delineating distinct communities within each. CA determines these communities' topical focuses, revealing how various groups prioritized different misinformation narratives throughout the pandemic. The findings demonstrate that influential, diverse communities introduce new misinformation, which then spreads to niche groups. This agenda-setting effect is amplified by social media algorithms optimized for engagement. The results provide valuable insights into how online communities shape public discourse during crises through the strategic dissemination of misinformation.

Whether or not U.S. women follow the recommended breast cancer screening guidelines is related to the perceived benefits and harms of the procedure. Twitter is a rich source of subjective information containing individuals' sentiment towards public health interventions/technologies. Using our modified version of Hutto and Gilbert (2014) sentiment classifier, we described the temporal, geospatial, and thematic patterns of public sentiment towards breast cancer screening with 8 months of tweets (n=64,524) in the U.S. To examine how sentiment was related to screening uptake behaviour, we investigated and identified significant associations between breast cancer screening sentiment (via Twitter) and breast cancer screening uptake (via BRFSS) at the state level.

Twitter influences political debates. Phenomena like fake news and hate speech show that political discourse on micro-blogging can become strongly polarised by algorithmic enforcement of selective perception. Some political actors actively employ strategies to facilitate polarisation on Twitter, as past contributions show, via strategies of 'hashjacking' 4. For the example of COVID-19 related hashtags and their retweet networks, we examine the case of partisan accounts of the German far-right party Alternative für Deutschland (AfD) and their potential use of 'hashjacking' in May 2020. Our findings indicate that polarisation of political party hashtags has not changed significantly in the last two years. We see that right-wing partisans are actively and effectively polarising the discourse by 'hashjacking' COVID-19 related hashtags, like #CoronaVirusDE or #FlattenTheCurve. This polarisation strategy is dominated by the activity of a limited set of heavy users. The results underline the necessity to understand the dynamics of discourse polarisation, as an active political communication strategy of the far-right, by only a handful of very active accounts.

Understanding how much two individuals are alike in their interests (i.e., interest similarity) has become virtually essential for many applications and services in Online Social Networks (OSNs). Since users do not always explicitly elaborate their interests in OSNs like Facebook, how to determine users' interest similarity without fully knowing their interests is a practical problem. In this paper, we investigate how users' interest similarity relates to various social features (e.g. geographic distance); and accordingly infer whether the interests of two users are alike or unalike where one of the users' interests are unknown. Relying on a large Facebook dataset, which contains 479, 048 users and 5, 263, 351 user-generated interests, we present comprehensive empirical studies and verify the homophily of interest similarity across three interest domains (movies, music and TV shows). The homophily reveals that people tend to exhibit more similar tastes if they have similar demographic information (e.g., age, location), or if they are friends. It also shows that the individuals with a higher interest entropy usually share more interests with others. Based on these results, we provide a practical prediction model under a real OSN environment. For a given user with no interest information, this model can select some individuals who not only exhibit many interests but also probably achieve high interest similarities with the given user. Eventually, we illustrate a use case to demonstrate that the proposed prediction model could facilitate decision-making for OSN applications and services.

The objective of this research is to examine how sentiment analysis can be employed to generate trading signals for the Foreign Exchange (Forex) market. The author assessed sentiment in social media posts and news articles pertaining to the United States Dollar (USD) using a combination of methods: lexicon-based analysis and the Naive Bayes machine learning algorithm. The findings indicate that sentiment analysis proves valuable in forecasting market movements and devising trading signals. Notably, its effectiveness is consistent across different market conditions. The author concludes that by analyzing sentiment expressed in news and social media, traders can glean insights into prevailing market sentiments towards the USD and other pertinent countries, thereby aiding trading decision-making. This study underscores the importance of weaving sentiment analysis into trading strategies as a pivotal tool for predicting market dynamics.

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected all 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions a...

In recent years, with the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of any control and verification mechanism has led to the spread of fake news, as one of the most important threats to democracy, economy, journalism and freedom of expression. Designing and using automatic methods to detect fake news on social media has become a significant challenge. In this paper, we examine the publishers' role in detecting fake news on social media. We also suggest a high accurate multi-modal framework, namely FR-Detect, using user-related and content-related features with early detection capability. For this purpose, two new user-related features, namely Activity Credibility and Influence, have been introduced for publishers. Furthermore, a sentence-level convolutional neural network is provided to combine these features with latent textua...

In a world of ever-increasing microblogs, the opinions, preferences, support, frustration, anger and other emotions of people regarding various events and individuals, surface in varied ways on social media. The purpose of this research is to find those hidden patterns in raw data, which can explain meaningful insights about its creation, the groups of people who created them and their sentiments which led to the generation of such data. Sentiment analysis has always been an effective methodology for discovering emotion and bias towards or against a situation, topic, thought or initiative and finding other meaningful insights from unstructured data. In this research, we attempted a type of document clustering wherein we attempted to classify the sentiments of the citizens of India as they micro-blogged their opinions, thoughts, views and ideas during the implication of the Citizenship Amendment Act (CAA) on the social networking site, Twitter. By analyzing the tweets of 13,000 twitter users during a specific timeline during which the discussion regarding the CAA was at its peak, we analyzed the sentiment of those twitter users by clustering their tweets (documents) into four sentiment groups with the help of Latent Dirichlet Allocation (LDA) which is an important tool for topic modelling in the domain of sentiment analysis. Using political ritual theory, the present paper examines the sentiments of people who tweeted during a protest in India. After the classification, our research also maps the online political behaviour of these 13,000 social media participants to the postulates of political ritual theory which is explained by previous research regarding the behaviour of physically co-existing political participants and also justifies this display of various sentiments regarding the CAA in the footsteps of political rituals.

The concept of “fake news” has become widespread in recent years, especially with the rise of the Internet. Fake news has become a worldwide phenomenon in the consumption of online information, as it is often designed to look like real news and is widely shared on social networks. Concerns regarding the possible detrimental effects of fake news on the public’s knowledge of events and topics, as well as on democracy and public discourse in general, have arisen as a result of the rise of social networks. This article aims to provide a summary of a scientific investigation of antisocial behavior from historical research, conceptual analysis, and qualitative research in the form of a case study method. With the aim of analyzing online forums and the concept of disinformation using fake news, its implications have consequences that provoke reflection on this phenomenon. In the results, we propose a framework for investigating and evaluating the concepts of fake news and its interaction w...

Advanced analytics in Tableau is a powerful approach to uncovering insights from data, enabling organizations to make data-driven decisions. By leveraging advanced features and functionalities, Tableau provides a comprehensive solution for analyzing complex data sets. by SARAVANAN MANI Overview of Tableau Software Tableau is a leading data visualization and business intelligence software that empowers users to create interactive and shareable dashboards. It offers a user-friendly interface, seamless integration with various data sources, and robust tools for data exploration and visualization. One of the key strengths of Tableau is its ability to simplify complex data into easily understandable visualizations.

Advanced analytics in Tableau is a powerful approach to uncovering insights from data, enabling organizations to make data-driven decisions. By leveraging advanced features and functionalities, Tableau provides a comprehensive solution for analyzing complex data sets. by SARAVANAN MANI Overview of Tableau Software Tableau is a leading data visualization and business intelligence software that empowers users to create interactive and shareable dashboards. It offers a user-friendly interface, seamless integration with various data sources, and robust tools for data exploration and visualization. One of the key strengths of Tableau is its ability to simplify complex data into easily understandable visualizations.

Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a disconnect between the practitioners of online social media analysis - usually social, political and communication scientists - and the accessibility to tools capable of handling large quantities of online data, and examining online users and their behavior. We present two tools,birdspotter and evently, for analyzing online users based on their involvement in retweet cascades. birdspotter provides a toolkit to measure social influence and botnets of Twitter users. While it leverages the multimodal information of tweets, such as text contents, evently augments the user measurement by modeling the temporal dynamics of information diffusions using self-exciting processes. Both tools are designed for users with a wide range of computer expertise and include tutorials and detailed documentation. We illustrate a case ...

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator ...

This paper proposes a rule-based method for automatic polarity detection over economic news texts, which proved suitable for detecting the sentiment in Portuguese economic news. The data used in our experiments consists of 400 manually annotated sentences extracted from economic news, used for evaluation, and about 90 thousand Portuguese economic news, extracted from two well-known Portuguese newspapers, covering the period from 2010 to 2020, that have been used for training our systems. In order to perform sentiment analysis of economic news, we have also tested the adaptation of existing pre-trained modules, and also performed experiments with a set of Machine Learning approaches, and self-training. Experimental results show that our rule-based approach, that uses manually written rules related to the economic context, achieves the best results for automatically detecting the polarity of economic news, largely surpassing the other approaches.

Fast propagation, ease-of-access, and low cost have made social media an increasingly popular means for news consumption. However, this has also led to an increase in the preponderance of fake news. Widespread propagation of fake news can be detrimental to society, and this has created enormous interest in fake news detection on social media. Many approaches to fake news detection use the news content, social context, or both. In this work, we look at fake news detection as a problem of estimating the credibility of both the news publishers and users that propagate news articles. We introduce a new approach called the credibility score-based model that can jointly infer fake news and credibility scores for publishers and users. We use a state-of-the-art statistical relational learning framework called probabilistic soft logic to perform this joint inference effectively. We show that our approach is accurate at both fake news detection and inferring credibility scores. Further, our model can easily integrate any auxiliary information that can aid in fake news detection. Using the FakeNewsNet 1 dataset, we show that our approach significantly outperforms previous approaches at fake news detection by up to 10% in recall and 4% in accuracy. Furthermore, the credibility scores learned for both publishers and users are representative of their true behavior. CCS CONCEPTS • Computing methodologies → Statistical relational learning; • Information systems → Social networks;

Fast propagation, ease-of-access, and low cost have made social media an increasingly popular means for news consumption. However, this has also led to an increase in the preponderance of fake news. Widespread propagation of fake news can be detrimental to society, and this has created enormous interest in fake news detection on social media. Many approaches to fake news detection use the news content, social context, or both. In this work, we look at fake news detection as a problem of estimating the credibility of both the news publishers and users that propagate news articles. We introduce a new approach called the credibility score-based model that can jointly infer fake news and credibility scores for publishers and users. We use a state-of-the-art statistical relational learning framework called probabilistic soft logic to perform this joint inference effectively. We show that our approach is accurate at both fake news detection and inferring credibility scores. Further, our model can easily integrate any auxiliary information that can aid in fake news detection. Using the FakeNewsNet 1 dataset, we show that our approach significantly outperforms previous approaches at fake news detection by up to 10% in recall and 4% in accuracy. Furthermore, the credibility scores learned for both publishers and users are representative of their true behavior. CCS CONCEPTS • Computing methodologies → Statistical relational learning; • Information systems → Social networks;

This research is framed in the field of laboratories citizens, especially those who are driven by private institutions of higher education. The variables are going to study the interaction of participants of laboratories, the use of technology and how to participate in a working group when different disciplines are integrated. The main objective is to analyze how the collaborative knowledge construction is achieved when you have a common problem or social impact. Since the mixed paradigm is to enrich the information to be retrieved and analyzed to answer the research question. The instruments are designed questionnaires, in-depth interviews, non-participant observation and analysis of the productions of the participants. This project seeks to find information to know in depth the mechanisms of collaborative knowledge construction. Such construction will be used as input for participants to be active in the ways of responding to societal challenges.

Topic modeling is a powerful technique for uncovering hidden patterns in large documents. It can identify themes that are highly connected and lead to a certain region while accounting for temporal and spatial complexity. In addition, sentiment analysis can determine the sentiments of media articles on various issues. This study proposes a two-stage natural language processing-based model that utilizes Latent Dirichlet Allocation to identify critical topics related to each type of legal case or judgment and the Valence Aware Dictionary Sentiment Reasoner algorithm to assess people's sentiments on those topics. By applying these strategies, this research aims to influence public perception of controversial legal issues. This study is the first of its kind to use topic modeling and sentiment analysis on Indian legal documents and paves the way for a better understanding of legal documents.

Topic modeling is a powerful technique for uncovering hidden patterns in large documents. It can identify themes that are highly connected and lead to a certain region while accounting for temporal and spatial complexity. In addition, sentiment analysis can determine the sentiments of media articles on various issues. This study proposes a two-stage natural language processing-based model that utilizes Latent Dirichlet Allocation to identify critical topics related to each type of legal case or judgment and the Valence Aware Dictionary Sentiment Reasoner algorithm to assess people's sentiments on those topics. By applying these strategies, this research aims to influence public perception of controversial legal issues. This study is the first of its kind to use topic modeling and sentiment analysis on Indian legal documents and paves the way for a better understanding of legal documents.

T oday, social media is one of the dominant channels for communicating and collaborating among individuals and organizations. People are using social media as an effective and inexpensive way to make friendships with new people and to keep in touch with the existing ones [1], [2]. Baruah [3] argued that social media has become an efficient and effective tool of communication in which individuals can share ideas and information, organizations can market their services and products, and customers can interact sharing their feedbacks and preferences. Hajli [4] introduced social commerce in his study in which the levels of interaction over social networks between consumers and businesses are beneficial. Another motive to use social media is education. Zaidieh [5] showed that convenience, flexibility, and accessibility are the motives behind using social media in education; students can easily participate in discussions and share their experiences. Searching for jobs is another motive that has been presented by Black and Johnson [6] who showed that employers use social networking sites during the recruitment process. With millions of hours spent on Facebook by the U.S. population, which is 18 times higher than the next biggest social network [7], Facebook is indeed an outstanding communication channel for millions of people. A study targeting 45 000 people aged 16-64 worldwide showed that Facebook has the largest share of social media users, where 82% of the study population has a Facebook account, and 42% of them are considered active users [8]. Shi et al. [9] investigated motives that influence users' intention to keep using Facebook; their results showed that keeping in touch with friends, entertainment and information seeking positively influenced users' satisfaction with Facebook. Additionally, Al-Menayes [10] presented entertainment, personal utility, information seeking, convenience and altruism as the main motives behind using social media. Concerns Nevertheless, despite Facebook's enormous popularity, concerns been raised previously in the literature. Privacy Privacy is one of the major users' concerns illustrated in the literature [11]. Stieger et al. [12] showed that privacy was one of the major reasons that led 48.3% of Facebook users to quit. Baumer et al. [13] mentioned that users are

Background and objective: Medical social networking platforms provide virtual spaces ensuring the interaction between different healthcare participants. As a part of the exchange, these spaces allow subscribers to upload medical images, describing different medical cases for an analysis or an interpretation proposal. Facing this expected huge amount of uploaded images generated daily, it is needed to engage new mechanisms to effectively deal with this circumstance, for enhancing the search function process of medical images, based on what is uploaded. To overcome this issue, setting up of images visual searching based on a content-based medical image retrieval scheme is the solution. More clearly, such mechanism will help and motivate medical social networking subscribers to find visually similar stored images. Methods: To ensure this task, the development of this mechanism, technically, is based mainly on a fusion of three visual features, which offers a flexible and more precision. It is reinforced by a weighted distance approach through attributing weights for feature vectors to scale up the performance. Indeed, the displayed results of this system can be updated based on user's intention by a user interactive feedback mechanism to indicate the truly relevant images. Results: We provide the theoretical performance of our scheme. Extensive experiments were conducted on a categorically classified collection containing 500 images. We conduct a practical evaluation on this dataset classes, putting returned results in a comparative study with other models results, existing in the literature. Conclusions: The proposed scheme preserves the efficiency of the search task. As theoretically and experimentally established, our scheme offers an effective image retrieval model that can support different subscribers' expectations. The relevance feedback mechanism can keep the dynamism of the system, thus offering a continuous searching result evolution. Experimentation outcomes indicate better findings compared with the other models.

Medical social networking sites enabled multimedia content sharing in large volumes, by allowing physicians and patients to upload their medical images. These images are diagnosed and commented, in different languages, by several specialists instantly. Moreover, it is necessary to employ new techniques, in order to automatically extract information and analyze knowledge from the huge number of comments expressing specialist's analyzes and recommendations. For this reason, we propose a termsbased method in order to extract the relevant terms and words which can describe the medical image. Furthermore, significant extracted terms and keywords will be used later to index medical images, in order to facilitate their search through the social network site. In fact, we need to take account that existing comments are expressed in different languages. So, it is essential to implement a multilingual indexation method to eliminate the ambiguity which will be the cause of the effectiveness's reduction of the search function. In order to palliate this situation, we propose a multilingual mixed approach which concentrates on algorithms based on statistical methods and external multilingual semantic resources, in order to handle and to cover different languages. The use of external resources, such as semantic multilingual thesaurus, can improve the efficiency of the indexing process. The proposed method can be applied in different languages. Our study is validated by a set of experiments and a comparison study with some existing approaches in literature. Experimental results have indicated that the proposed system has a superior performance compared to other systems. Finally, we need also to verify whether the system is considered by the specialists as a potential aid. We evaluate its impact, by inquiring the specialists about the degree of confidence in our system. By analyzing the obtained results, we can argue that the proposed system presented a high acceptance and viability rate regarding the specialists' interests in the annotation practice domain.

With a network approach, we examine the case of the German far-right party Alternative für Deutschland (AfD) and their potential use of a "hashjacking" strategy 4. Our findings suggest that rightwing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by "hashjacking" the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (e.g. migration), but as a direct consequence of their digital political communication strategy.

Content-Based Image Retrieval (CBIR) is also known as Query By Image Content (QBIC) is the application of computer vision techniques and it gives solution to the image retrieval problem such as searching digital images in large databases. The need to have a versatile and general purpose Content Based Image Retrieval (CBIR) system for a very large image database has attracted focus of many researchers of informationtechnology-giants and leading academic institutions for development of CBIR techniques. Due to the development of network and multimedia technologies, users are not fulfilled by the traditional information retrieval techniques. So nowadays the Content Based Image Retrieval (CBIR) are becoming a source of exact and fast retrieval. Texture and color are the important features of Content Based Image Retrieval Systems. In the proposed method, images can be retrieved using color-based, texture-based and color and texture-based. Algorithms such as auto color correlogram and correlation for extracting color based images, Gaussian mixture models for extracting texture based images. In this study, Query point movement is used as a relevance feedback technique for Content Based Image Retrieval systems. Thus the proposed method achieves better performance and accuracy in retrieving images.

Interaction networks are generally much less homophilic than affiliation networks, accommodating for many more cross-cutting links. By statistically assigning a political valence to users from their networklevel affiliation patterns, and by further contrasting interaction and affiliation (quotes and retweets) within specific discursive events, namely quote trees, we describe a variety of cross-cutting patterns which significantly nuance the traditional "echo chamber" narrative.

Over the years the stock market has proved to be very difficult to predict due to its unpredictable activities. Data mining techniques such as clustering, decision trees, genetic algorithms and artificial neural networks have been used in order to predict the stock market. Although there has been a significant amount of research done in this area, there are still many issues that have not been explored yet. The impact of fundamental analysis in the prediction of the stock market has been ignored though it can play a vital role in the prediction of the stock market. In this research, the problem of how a social data sentiment correlates to stock price is studied. A stock price prediction model was built using social data sentiments to predict the stock market. Sentiments analysis principles were applied to machine learning techniques in order to find the correlation between the stock market and public sentiments. This study particularly intended to assess the predictability of prices on the Botswana Stock Exchange through the application of Facebook sentiments classification. Three classification models were created that depicted news polarity as happy, calm, alert and vital. Results show that Naïve Bayes and Support vector machine performed well in both types of testing as compared to Random Forest. Naïve Bayes gave good results in terms of error margins with an accuracy of 83.3% making it the best classifier for our data set. When plotting the time series plot of sentiment scores and comparing it to the actual stock price graph, a conclusion can be reached that sentiments and stock prices are related and thus stock prices can be predicted using sentiments.

Facebook is the most pervasive social network in the world. While, we would commonly expect everyone who has access to the Internet to use Facebook, some people have chosen not to join or to opt-out of its services. This qualitative study is an effort to understand the justifications that drive their decision. Based on in-depth semi-structured interviews, several recurring reasons were identified for Facebook non-use. Some results obtained are truly consistent with previous research. These include perceived security issues and safety risks, absence of trust, privacy concerns, influence of important others, lack of control, uselessness, availability of alternatives, lack of interest, and inappropriate content. However, some findings, such as one's attitude toward the Facebook company and online social connectedness, diverge from previously published work. There is also enough evidence in the results to support the existence of additional contributing reasons for the rejection of Facebook that have not been directly reported before. These include religious beliefs, influence of culture, and a concern toward one's information being available online: a point that we do put forward for further inquiry.