Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

SOCIAL WEB (61 journals)

Showing 1 - 58 of 58 Journals sorted alphabetically
ACM Transactions on Social Computing     Hybrid Journal  
ACM Transactions on the Web (TWEB)     Hybrid Journal   (Followers: 3)
American Journal of Information Systems     Open Access   (Followers: 4)
Asiascape : Digital Asia     Hybrid Journal   (Followers: 1)
CCF Transactions on Networking     Hybrid Journal  
Communications in Mobile Computing     Open Access   (Followers: 14)
Computational Social Networks     Open Access   (Followers: 4)
Cyberpolitik Journal     Open Access  
Cyberpsychology, Behavior, and Social Networking     Hybrid Journal   (Followers: 16)
Data Science     Open Access   (Followers: 6)
Digital Library Perspectives     Hybrid Journal   (Followers: 42)
Discover Internet of Things     Open Access   (Followers: 2)
Informação & Informação     Open Access   (Followers: 2)
Information Technology and Libraries     Open Access   (Followers: 342)
Infrastructure Complexity     Open Access   (Followers: 5)
International Journal of Art, Culture and Design Technologies     Full-text available via subscription   (Followers: 10)
International Journal of Bullying Prevention     Hybrid Journal   (Followers: 1)
International Journal of Digital Humanities     Hybrid Journal   (Followers: 3)
International Journal of e-Collaboration     Full-text available via subscription  
International Journal of E-Entrepreneurship and Innovation     Full-text available via subscription   (Followers: 6)
International Journal of Entertainment Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Information Privacy, Security and Integrity     Hybrid Journal   (Followers: 25)
International Journal of Information Technology and Web Engineering     Hybrid Journal   (Followers: 2)
International Journal of Interactive Communication Systems and Technologies     Full-text available via subscription   (Followers: 2)
International Journal of Interactive Mobile Technologies     Open Access   (Followers: 8)
International Journal of Internet and Distributed Systems     Open Access   (Followers: 2)
International Journal of Knowledge Society Research     Full-text available via subscription  
International Journal of Networking and Virtual Organisations     Hybrid Journal   (Followers: 11)
International Journal of Social and Humanistic Computing     Hybrid Journal  
International Journal of Social Computing and Cyber-Physical Systems     Hybrid Journal  
International Journal of Social Media and Interactive Learning Environments     Hybrid Journal   (Followers: 14)
International Journal of Social Network Mining     Hybrid Journal   (Followers: 3)
International Journal of Virtual Communities and Social Networking     Full-text available via subscription   (Followers: 1)
International Journal of Web Based Communities     Hybrid Journal  
International Journal of Web-Based Learning and Teaching Technologies     Hybrid Journal   (Followers: 20)
International Journal on Semantic Web and Information Systems     Hybrid Journal   (Followers: 4)
Internet Technology Letters     Hybrid Journal  
JLIS.it     Open Access   (Followers: 7)
Journal of Cyber Policy     Hybrid Journal   (Followers: 1)
Journal of Digital & Social Media Marketing     Full-text available via subscription   (Followers: 18)
Journal of Social Structure     Open Access   (Followers: 1)
Medicine 2.0     Open Access   (Followers: 2)
Observatorio (OBS*)     Open Access  
Online Social Networks and Media     Hybrid Journal   (Followers: 9)
Policy & Internet     Hybrid Journal   (Followers: 11)
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies     Hybrid Journal  
Redes. Revista Hispana para el Análisis de Redes Sociales     Open Access  
RESET     Open Access  
Scientific Phone Apps and Mobile Devices     Open Access  
Social Media + Society     Open Access   (Followers: 25)
Social Network Analysis and Mining     Hybrid Journal   (Followers: 4)
Social Networking     Open Access   (Followers: 3)
Social Networks     Hybrid Journal   (Followers: 20)
Social Science Computer Review     Hybrid Journal   (Followers: 13)
Synthesis Lectures on the Semantic Web: Theory and Technology     Full-text available via subscription  
Teknokultura. Revista de Cultura Digital y Movimientos Sociales     Open Access  
Terminal     Open Access  
Texto Digital     Open Access  
Similar Journals
Journal Cover
Social Network Analysis and Mining
Journal Prestige (SJR): 0.306
Citation Impact (citeScore): 1
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1869-5450 - ISSN (Online) 1869-5469
Published by Springer-Verlag Homepage  [2469 journals]
  • Lifetime of tweets: a statistical analysis

    • Free pre-print version: Loading...

      Abstract: Abstract Social media has become such a large part of people’s life that even if little at a time, that influence can accommodate over time and can manipulate or even form new opinions. The authors have gathered data with which it is easily understood that the growth of Twitter, the people within its engagement range and its potential for becoming a portal of information sourcing as well as incidents have grown considerably well over the last decade and are well expected to grow into the next decade as well due to the new generation telecom technologies. This study aims to understand how much time Twitter trends remain ‘hot’ based on various parameters including but not limited to demography, the incident, time period or the people affected.The main objective is to gather data about different trending topics over different time periods and then analyze the pattern of how tweet volume due to that Twitter trend increased or decreased over a few days. This allows to demonstrate that Twitter can be a powerful tool to manipulate public opinion since this reaches a large number of users in a lot of developed countries. The influence of tweets can be seen from the fact that even a tweet done from a non-influential person’s account can garner enough attention to become worldwide phenomenon. Towards the end of the study, the authors used a visual medium to depict how various topics fared over the 5 days that tweets were scraped.
      PubDate: 2022-08-04
       
  • FA-Net: fused attention-based network for Hindi English code-mixed
           offensive text classification

    • Free pre-print version: Loading...

      Abstract: Abstract Widespread usage of social media platforms like Twitter, Facebook, and YouTube allows sharing of opinions and suggestions across countries. On the contrary, these platforms are often misused to disseminate hate speech and offensive content. Moreover, in a multilingual society such as India, many users resort to code-mixing while typing on social media. Thus, we have focused on Hindi English (Hi–En) Code-Mixed hate speech and offensive text classification. Recently, numerous approaches have emerged, and most of these approaches use CNN and LSTM in a stacked manner to extract local and sequential semantic features. However, these arrangements diminish the comprehensive effect of local and sequential features. In addition, deep framework suffers from issue of vanising gradient. Therefore, in our work, we have proposed, local and sequential knowledge aware Fused Attention-based Network (FA-Net), which introduces a fusion of attention mechanism of collective and mutual learning between local and sequential features. The proposed network (FA-Net) is lower in depth more in breadth in comparison to the existing architectures. It has three building blocks: Code Mixed Hybrid Embedding, Locally Driven Sequential Attention-2 (LDSA-2), Locally Driven Sequential Attention-3 (LDSA-3). CMHE is developed using customized Hi-En code mixed data, aiming the network to initialize with relevant weights. LDSA-2 and LDSA-3 equip the model to build a comprehensive representation having past, future, and local contextual knowledge w.r.t any location in the sentence. Extensive experimentation on two benchmark datasets shows that FA-Net has outperformed other state of the art.
      PubDate: 2022-08-03
       
  • Performance analysis of transformer-based architectures and their
           ensembles to detect trait-based cyberbullying

    • Free pre-print version: Loading...

      Abstract: Abstract The influence of social media is one of the most dominating characteristics of the current era, and this has led cyberbullying to grow into a more serious social issue. As a result, automated cyberbullying detection systems need to be an integral part of almost all social media platforms. Past studies on this domain have primarily focused on hand-picked features and traditional machine learning approaches for cyberbullying detection from user comments on social media. Recently, transformers have been proved to be quite effective in various language-related tasks; however, their effectiveness has not been extensively explored in this particular domain. In this study, we evaluate the individual performance of several well-known transformer-based architectures and aim to contribute to the development of automated cyberbullying detection systems by proposing our own transformer-based ensemble framework. Our proposed framework is evaluated on a balanced and an imbalanced dataset, both of which are constructed from a large collection of Twitter comments and are publicly available. Our proposed architecture outperforms all the baseline models, as well the individual standalone classifier networks that are used in our ensemble, obtaining an average F1-score of 95.92% on the balanced dataset and an average F1-score of 87.51% on the imbalanced dataset. We further investigate the cases where our proposed architecture misclassifies samples from both datasets, preventing it from achieving a perfect score. Our models and code have been made publicly available (https://github.com/tasnim7ahmed/Extended-Cyberbullying-Detection/).
      PubDate: 2022-08-02
       
  • Credibility aspects’ perceptions of social networks, a survey

    • Free pre-print version: Loading...

      Abstract: Abstract Social networks are currently considered the universally superior information source for people as well as organizations. This source is considered as one of the main components featuring an immense variety of successful applications that support different contexts. During disasters, social networks have been vital actors in information dissemination as they, not surprisingly, gained a critical role in the communities’ networks. Social networks not only provide a flood of information, but this information is naturally supported by the participants’ opinions, sentiments, and emotions. Therefore, ensuring an acceptable level of credibility for social media information is a vital subject to discuss. This research aims at highlighting the role of social networks in different fields such as in the health field and retailing field. Before discussing this role, the research provided many other aspects in this scope including the affecting factors on the information credibility, the role of the key actors in raising the level of credibility, and others. The research highlighted the contributing techniques in evaluating the level of credibility as well as their influence level. Moreover, different perspectives of information credibility are discussed including social networks’ credibility, email, news, and opinions’ credibility. Finally, the research discussed the effect of credibility in the health field. The researchers of this paper argue that the included summaries of the researches effectively contribute to an understanding of the field gaps and is a great value for the researchers in the same field to gain a higher understanding of the credibility, the nature of the social networks’ contents, and the manipulation aspects.
      PubDate: 2022-08-01
       
  • Mining and modelling temporal dynamics of followers’ engagement on
           online social networks

    • Free pre-print version: Loading...

      Abstract: Abstract A relevant fraction of human interactions occurs on online social networks. In this context, the freshness of content plays an important role, with content popularity rapidly vanishing over time. We therefore investigate how influencers’ generated content (i.e., posts) attracts interactions, measured by the number of likes or reactions. We analyse the activity of influencers and followers over more than 5 years, focusing on two popular social networks: Facebook and Instagram, including more than 13 billion interactions and about 4 million posts. We investigate the influencers’ and followers’ behaviour over time, characterising the arrival process of interactions during the lifetime of posts, which are typically short-lived. After finding the factors playing a crucial role in the post popularity dynamics, we propose an analytical model for the user interactions. We tune the parameters of the model based on the past behaviour observed for each given influencer, discovering that fitted parameters are pretty similar across different influencers and social networks. We validate our model using experimental data and effectively apply the model to perform early prediction of post popularity, showing considerable improvements over a simpler baseline.
      PubDate: 2022-07-31
       
  • Investigating political polarization in India through the lens of Twitter

    • Free pre-print version: Loading...

      Abstract: Abstract Social media plays a pivotal role in shaping communication among political entities. Substantial research has been carried out for examining the impact of politicians’ social media usage and interactions on political polarization. Analysing political polarization is particularly significant for fragmented political systems like India where collaboration between parties is essential for winning support in parliament. Different topics of discussion between political entities may induce different levels of polarization. This study aims to examine the presence of polarization on Twitter social media platform with respect to different topics of political discussions among Indian politicians. The investigation is based upon two conflicting notions about social media in influencing political polarization. The first notion regards social media as a medium for interaction between different ideological users. The second opinion on the other hand focuses on prevalence of selective exposure in social media leading to polarization. The study will investigate the use of Twitter for forming communication ties in and between parties and the extent of divergence of opinions during political discourse. The investigation performs social network analysis and content analysis of the tweets posted by Indian politicians during some major events in India from 2019 to 2021. For an unbiased topic-specific analysis of polarization, some important topics related to Indian government policies, national security and natural disaster events have been considered. The findings of the study suggest that Twitter not only opens up communication spaces to Indian political users but also makes online political discussions among them polarized. Moreover, the extent of polarization varies with respect to topics of political discussions. Polarization is more for controversial and debatable topics than non-controversial ones.
      PubDate: 2022-07-31
       
  • Towards a standard modeling of social health care practice

    • Free pre-print version: Loading...

      Abstract: Alliance among healthcare professionals grows and develops during the practice of healthcare services, clearly in COVID19 epidemic. This joint effort makes one’s way gradually and systematically, especially in the virtual field of activities. This manner of exchanges result the coming to light of health-related social network sites, committed to all participants in these tasks (healthcare givers and takers). It introduces a creative method of medical caring referred to as Social Health (S-health). S-health can be invested as a technological outcome, in a collaborative and participatory mode, around healthcare takers treatment, supervision and safe preservation. The objective is to offer a constant improvement of the quality of care given to both personal and community health practices. In several regards, S-health can serve with this aim by the authorization of interactive movements of the medical information between both health care takers and givers. In fact, the dynamism of situated health social care models is crucial, which creates surpassing and remarkable challenges, related to the setting up of online health care-centered systems. As expected, none of the main social networks sites has yet discovered how to merge all health care tasks straight to their platforms. In such context, we need to deal with the insufficiency of an overall framework to establish S-health platforms properties from both medical and IT interpretations. This deficiency necessitates a modeling and a design arrangement dedicated to handle these platforms. So, the creation of a modeling structure, to represent S-health platforms features, has considerable prospect to reply this defiance and to take advantage of the magnificent potential of social networking, via a fundamental representation of S-health constituents in an abstract conceptual template. The modeling map demands to fulfill an amount of norms and points of reference regarding technological restrictions and design necessities, before the initiation task. It is the most relevant interest in the successful and effective structuring of an S-health virtual space.
      PubDate: 2022-07-30
       
  • Applications of machine learning for COVID-19 misinformation: a systematic
           review

    • Free pre-print version: Loading...

      Abstract: Abstract The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people. To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques. The objective of this study is to systematically review, assess, and synthesize state-of-the-art research articles that have used different ML and DL techniques to detect COVID-19 misinformation. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey was solely centered on reproducible and high-quality research. We reviewed 43 papers that fulfilled our inclusion criteria out of 260 articles found from our keyword search. We have surveyed a complete pipeline of COVID-19 misinformation detection. In particular, we have identified various COVID-19 misinformation datasets and reviewed different data processing, feature extraction, and classification techniques to detect COVID-19 misinformation. In the end, the challenges and limitations in detecting COVID-19 misinformation using ML techniques and the future research directions are discussed.
      PubDate: 2022-07-29
       
  • Feature selection from disaster tweets using Spark-based parallel
           meta-heuristic optimizers

    • Free pre-print version: Loading...

      Abstract: Abstract Twitter is considered a useful tool for effective tracking and management of disaster-related incidents. However, due to a large number of irrelevant features in textual data, the problem of high dimensionality arises which eventually increases the computational cost and also decreases the classification performance. Thus to handle such type of problem, this work presents Spark-BGWO and Spark-BWOA, an Apache Spark-based parallel implementation of two nature inspired meta-heuristic optimizers, binary gray wolf optimization (BGWO) and binary whale optimization algorithm (BWOA) for optimal feature selection and classification of disaster tweets. Random forests (RF) classifier is applied during wrapper-based feature subset selection and classification process. The performance of proposed optimizers was analyzed on seven benchmark disaster tweet datasets, namely California Wildfires, Hurricane Harvey, Hurricane Irma, Hurricane Maria, Iraq–Iran Earthquake, Mexico Earthquake, and Sri Lanka Floods, and then results were compared with the most recent work on the same datasets. Results showed that both optimizers performed competently in feature selection and classification process, as well as outperform the results of previous work over five out of seven datasets in terms of accuracy and F1-score.
      PubDate: 2022-07-28
       
  • Effect of public sentiment on stock market movement prediction during the
           COVID-19 outbreak

    • Free pre-print version: Loading...

      Abstract: Abstract Forecasting the stock market is one of the most difficult undertakings in the financial industry due to its complex, volatile, noisy, and nonparametric character. However, as computer science advances, an intelligent model can help investors and analysts minimize investment risk. Public opinion on social media and other online portals is an important factor in stock market predictions. The COVID-19 pandemic stimulates online activities since individuals are compelled to remain at home, bringing about a massive quantity of public opinion and emotion. This research focuses on stock market movement prediction with public sentiments using the long short-term memory network (LSTM) during the COVID-19 flare-up. Here, seven different sentiment analysis tools, VADER, logistic regression, Loughran–McDonald, Henry, TextBlob, Linear SVC, and Stanford, are used for sentiment analysis on web scraped data from four online sources: stock-related articles headlines, tweets, financial news from "Economic Times" and Facebook comments. Predictions are made utilizing both feeling scores and authentic stock information for every one of the 28 opinion measures processed. An accuracy of 98.11% is achieved by using linear SVC to calculate sentiment ratings from Facebook comments. Thereafter, the four estimated sentiment scores from each of the seven instruments are integrated with stock data in a step-by-step fashion to determine the overall influence on the stock market. When all four sentiment scores are paired with stock data, the forecast accuracy for five out of seven tools is at its most noteworthy, with linear SVC computed scores assisting stock data to arrive at its most elevated accuracy of 98.32%.
      PubDate: 2022-07-27
       
  • Public opinion monitoring through collective semantic analysis of tweets

    • Free pre-print version: Loading...

      Abstract: Abstract The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism, consisting of a semantic descriptor that relies on Natural Language Processing algorithms. A four-dimensional descriptor is first extracted for each tweet independently, quantifying text polarity, offensiveness, bias and figurativeness. Subsequently, it is summarized across multiple tweets, according to a desired aggregation strategy and aggregation target. This can then be exploited in various ways, such as training machine learning models for forecasting day-by-day public opinion predictions. The proposed mechanism is applied to the 2016/2020 US Presidential Elections tweet datasets and the resulting succinct public opinion descriptions are explored as a case study.
      PubDate: 2022-07-26
       
  • Deep learning based topic and sentiment analysis: COVID19 information
           seeking on social media

    • Free pre-print version: Loading...

      Abstract: Abstract Social media platforms have become a common place for information exchange among their users. People leave traces of their emotions via text expressions. A systematic collection, analysis, and interpretation of social media data across time and space can give insights into local outbreaks, mental health, and social issues. Such timely insights can help in developing strategies and resources with an appropriate and efficient response. This study analysed a large Spatio-temporal tweet dataset of the Australian sphere related to COVID19. The methodology included a volume analysis, topic modelling, sentiment detection, and semantic brand score to obtain an insight into the COVID19 pandemic outbreak and public discussion in different states and cities of Australia over time. The obtained insights are compared with independently observed phenomena such as government-reported instances.
      PubDate: 2022-07-25
       
  • Fighting hate speech from bilingual hinglish speaker’s perspective, a
           transformer- and translation-based approach.

    • Free pre-print version: Loading...

      Abstract: Abstract Many people have begun to use social media platforms due to the increased use of the Internet over the previous decade. It has a lot of benefits, but it also comes with a lot of risks and drawbacks, such as Hate speech. People in multilingual societies, such as India, frequently mix their native language with English while speaking, so detecting hate content in such bilingual code-mixed data has drawn the larger interest of the research community. The majority of previous work focuses on high-resource language such as English, but very few researchers have concentrated on the mixed bilingual data like Hinglish. In this study, we investigated the performance of transformer models like IndicBERT and multilingual Bidirectional Encoder Representation(mBERT), as well as transfer learning from pre-trained language models like ULMFiT and Bidirectional encoder Representation(BERT), to find hateful content in Hinglish. Also, Transformer-based Interpreter and Feature extraction model on Deep Neural Network (TIF-DNN), is proposed in this work. The experimental results found that our proposed model outperforms existing state-of-art methods for Hate speech identification in Hinglish language with an accuracy of 73%.
      PubDate: 2022-07-24
       
  • Favorite YouTubers as a source of health information during quarantine:
           viewers trust their favorite YouTubers with health information

    • Free pre-print version: Loading...

      Abstract: Abstract During quarantine, between March and May 2020, YouTubers disseminated information about Covid-19 and the quarantine. The objectives of this study are (1) to explore whether YouTubers are considered as reliable sources of information regarding quarantine by French social media users, (2) to evaluate the link between the parasocial relationship with a favorite YouTuber and the level of trust in the information provided by this YouTuber and (3) to test the effectiveness of the favorite YouTuber as a source of information about the benefit of quarantine. Data from 596 participants were collected through an online survey, among whom, 251 had a favorite YouTuber. Wilcoxon signed-rank tests and ordered logistic regressions were used to explore two research questions and to test two hypotheses. Results show that (1) information about the benefit of quarantine from the favorite YouTuber is considered just as reliable as information from journalists, friends or family members, (2) the intensity of the parasocial relationship with the favorite YouTuber is positively and significantly associated with the level of trust in that favorite YouTuber, (3) having received trusted information about the benefit of quarantine from one’s favorite YouTubers is positively and significantly associated with the perception of the utility of quarantining. This study identifies YouTubers as important sources for health communication.
      PubDate: 2022-07-24
       
  • An effective short-text topic modelling with neighbourhood
           assistance-driven NMF in Twitter

    • Free pre-print version: Loading...

      Abstract: Abstract Social media such as Twitter connect billions of people by allowing them to exchange their thoughts via short-text communication. Topic modelling is a widely used technique for analysing short texts. Discovering topic clusters in short-text collections faces issues with distance-based, density-based and dimensionality reduction-based methods due to their higher dimensionality and short length which results in extremely sparse text representation matrices. We propose the ‘neighbourhood-based assistance’-driven non-negative matrix factorization (NMF) method to handle high-dimensional sparse short-text representation with lower-dimensional projection effectively. We utilized NMF that aligned with the natural non-negativity of text data coupled with the symmetric document affinity information to identify topic distribution in the short text. Neighbourhood information within documents is captured using Jaccard similarity to assist information loss, resulting in higher-to-lower-dimensional projection. Experimental results with Twitter data sets show that the proposed approach is able to attain high accuracy compared to state-of-the-art methods quantitatively, while qualitative analysis with case studies validates the ability of the proposed approach in generating meaningful topic clusters.
      PubDate: 2022-07-24
       
  • Survival analysis for user disengagement prediction:
           question-and-answering communities’ case

    • Free pre-print version: Loading...

      Abstract: Abstract We used survival analysis to model user disengagement in three distinct questions-and-answering communities in this work. We used the complete historical data from domains including Politics, Data Science, and Computer Science from Stack Exchange communities from their inception until May 2021, including information about all users who were members of one of these three communities. Furthermore, in formulating the user disengagement prediction as a survival analysis task, we employed two survival analysis techniques (Kaplan–Meier and random survival forests) to model and predicted the probabilities of members of each community becoming disengaged. Our main finding is that the likelihood of users with even a few contributions staying active is noticeably higher than those who were making no contributions; this distinction may widen as time passes. Moreover, the results of our experiments indicate that users with more favourable views toward the content shared on the platform may stay engaged longer. Finally, regardless of their themes, the observed pattern holds for all three communities.
      PubDate: 2022-07-22
       
  • Link segmentation entropy for measuring the network complexity

    • Free pre-print version: Loading...

      Abstract: Abstract Measuring the network complexity has been addressed in many studies in graph theory. In the context of complex networks, a class of complexity measures has been proposed based on structure entropy. Although the proposed measures can quantify the complexity of different networks, they are mainly focused on the nodes structural properties and ignore the links information. This is while the link analysis plays a crucial role in studying and comprehension of different types of complex networks. In this paper, we propose to employ the similarity-based link prediction measures to capture the links information and quantify it as an entropy measure. The findings of the experimental study based on several synthetic as well as real-world networks demonstrate the validity and effectiveness of the proposed complexity measure.
      PubDate: 2022-07-21
       
  • Design of a personalized recommender system using sentiment analysis in
           social media (case study: banking system)

    • Free pre-print version: Loading...

      Abstract: Abstract Customer retention and finding a way to preserve the customers are the most important issues of any organization. The main purpose of the present study in machine learning is to focus on correctly identifying customer needs with a method based on extracting opinion and sentiment analysis and quantifying customers’ sentiment orientation. In other words, the main issue is designing a recommender system to provide appropriate services according to customer satisfaction, sentiment, and experiences. The proposed method is based on customers’ opinions and experiences, which are obtained by evaluating tweets containing hashtags with the titles and headings of banking services as a statistical population. So, after reconsideration, correlation scores in terms of people’s sentiment score due to the tweets, cosine similarity, and reliability, consideration of relevant characteristic groups as well as recorded ideas in the training and testing process will be provided in the form of submitting a personalized offer to receive banking services. In order to represent a recommending solution, suitable classification methods are used along with the opinion mining methods and proper validation approach as well, and the terminal designed system with a little error will take steps to provide personalized services as well as help the banking system. As there is no thorough provision of banking services tailored to the customers’ situation, therefore, the mentioned system will be extremely beneficial.
      PubDate: 2022-07-19
       
  • Analyzing voter behavior on social media during the 2020 US presidential
           election campaign

    • Free pre-print version: Loading...

      Abstract: Abstract Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.
      PubDate: 2022-07-18
       
  • Indexing complex networks for fast attributed kNN queries

    • Free pre-print version: Loading...

      Abstract: Abstract The k nearest neighbor (kNN) query is an essential graph data-management tool used for finding relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response and thus are promising approaches. However, they struggle to handle large-scale attributed complex networks. This is because constructing indices and querying kNN nodes in large-scale networks are computationally expensive, and they are not designed to handle node attributes included in the networks. In this paper, we propose a novel graph indexing algorithm, namely CT index, for fast kNN queries on large complex networks. To overcome the aforementioned limitations, our algorithm generates two types of indices based on the topological properties of complex networks. In addition, we further propose BAG index along with CT index so that our algorithm enables to explore kNN nodes based on the attribute similarity. Our extensive experiments on real-world graphs show that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than other state-of-the-art methods.
      PubDate: 2022-07-16
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.235.140.84
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-