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Weaver, Jesse, and Paul Tarjan. "Facebook Linked Data via the Graph API." Semantic Web 4, no. 3 (2013): 245–50. http://dx.doi.org/10.3233/sw-2012-0078.
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Alsaif, Suleiman Ali, Adel Hidri, and Minyar Sassi Hidri. "Towards Inferring Influential Facebook Users." Computers 10, no. 5 (May 9, 2021): 62. http://dx.doi.org/10.3390/computers10050062.
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Because of the complexity of the actors and the relationships between them, social networks are always represented by graphs. This structure makes it possible to analyze the effectiveness of the network for the social actors who are there. This work presents a social network analysis approach that focused on processing Facebook pages and users who react to posts to infer influential people. In our study, we are particularly interested in studying the relationships between the posts of the page, and the reactions of fans (users) towards these posts. The topics covered include data crawling, graph modeling, and exploratory analysis using statistical tools and machine learning algorithms. We seek to detect influential people in the sense that the influence of a Facebook user lies in their ability to transmit and disseminate information. Once determined, these users have an impact on business for a specific brand. The proposed exploratory analysis has shown that the network structure and its properties have important implications for the outcome of interest.
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Procaci, Thiago Baesso, Sean Wolfgand M. Siqueira, and Leila Cristina Vasconcelos de Andrade. "Finding Experts on Facebook Communities." International Journal of Knowledge Society Research 5, no. 2 (April 2014): 7–19. http://dx.doi.org/10.4018/ijksr.2014040102.
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Online communities have become important places for users to share information. In this context, the work described in this article aims to analyze computational methods that could allow us to identify users with the highest expertise levels on a specific knowledge domain in an online community. In this study the authots extracted data from a Java discussion group from an online community - Facebook, captured some important information and represented the community as a graph. Then, the authors compared the Bow-tie structure of this community with the ones from the Web and from a forum that are described in the literature. In addition, the authors tested some graph metrics and algorithms in order to analyze if they could provide a method to find the experts in this online community. The results show that four of the tested metrics can indicate if a user is an expert or not.
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Xu, Jin, Yu Zhong, and Bo Peng. "Parallel k-Way Partitioning Approach for Large Graphs." Advanced Materials Research 912-914 (April 2014): 1309–12. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1309.
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With the emergence of large social networks, such as Facebook and Twitter, graphs with millions to billions vertices are common. Instead of processing the network within a single machine, all the applications related are intended to be done in a distributed way using a cluster of commodity machines. In this paper, we study the parallel graph partitioning problem, which is the fundamental operation for large graphs. With the help of Hadoop/MapReduce, we propose aparallel k-way partitioningapproach. Unlike the previous ones, which require enough memory to keep the whole graph data within, our novel approach breaks such limitations. Also, due to the distributed nature, it is easy to integrate our partitioning approach into existed parallel platforms. We conduct extensive experiments on real graphs and synthetic graphs. All the experimental results prove the effectiveness and efficiency of our approach.
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Banerjee, Arijeet, and Pijush Kanti Kumar. "Review of Shortest Path Algorithm." International Journal of Computer Science and Mobile Computing 11, no. 4 (April 30, 2022): 1–8. http://dx.doi.org/10.47760/ijcsmc.2022.v11i04.001.
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Shortest path algorithm. Graphs are an example of non-linear data structure. A graph is a collection of nodes which are connected by edges. The definition of graph G = (V, E) is basically a collection of vertices and edges. Graphs can be classified on the basis of types of edges. Directed graphs have each of the edges directed which means the edges connecting the two nodes defines the way it is connected from and to. On the other side, undirected graphs have edges which have no direction. The edges of a graph have weights which are associated with it. The weight of an edge can be thought as the cost of the edge. Let’s assume there are two vertices representing two cities, then the weight of the edge between the vertices may represent the distance between the cities. Given a given graph and a particular node, we can find a path of least total weight from that node to other vertices of the graph. The total weight of the path will be the sum of the weights of the edges. Graphs can be used in real life to find the shortest path between two destinations, used in social networking sites like facebook and the world wide web where the web pages are represented by the nodes. Dijkstra’s Algorithm, Floyd – Warshall, Bellman Ford Algorithm, Johnson’s algorithm, A* search algorithm are some of the shortest path algorithms.
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Das, Sauvik, and Adam Kramer. "Self-Censorship on Facebook." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (August 3, 2021): 120–27. http://dx.doi.org/10.1609/icwsm.v7i1.14412.
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We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last-minute self-censorship in the time period, and provide specific evidence supporting the theory that a user’s “perceived audience” lies at the heart of the issue: posts are censored more frequently than comments, with status updates and posts directed at groups censored most frequently of all sharing use cases investigated. Furthermore, we find that: people with more boundaries to regulate censor more; males censor more posts than females and censor even more posts with mostly male friends than do females, but censor no more comments than females; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general.
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Zhang, Yilin, Marie Poux-Berthe, Chris Wells, Karolina Koc-Michalska, and Karl Rohe. "Discovering political topics in Facebook discussion threads with graph contextualization." Annals of Applied Statistics 12, no. 2 (June 2018): 1096–123. http://dx.doi.org/10.1214/18-aoas1191.
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Terrana, Diego, Agnese Augello, and Giovanni Pilato. "Analysis of Facebook Users' Relationships Through Sentiment Classification: A Case Study of Italian Politicians." International Journal of Semantic Computing 08, no. 03 (September 2014): 301–17. http://dx.doi.org/10.1142/s1793351x14400108.
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We illustrate a system that analyzes the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. We have focused the analysis on three main actors of Italian politics. The goal is to find people who agree or disagree about given topics with the owner of the Facebook page under analysis. All public posts shared by a user are retrieved by an ad hoc built crawler. Information such as 'posts', 'comments', 'likes', are extracted from the Facebook page. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each Facebook user under analysis a statistics of the topics dealt with is made, and for each category a graph is created where the concordance of sentiment is highlighted between the posts belonging to a given class and the related comments of the people interacting with the user or group under analysis. The graph can therefore be used to profile the user relationships according to sentiment classification.
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Nugroho, Arif, and Anna Agustina. "Examining Corporate Engagement in Social Media: Advancing The Use of Facebook for Corporation Page." CoverAge: Journal of Strategic Communication 10, no. 2 (March 29, 2020): 1–10. http://dx.doi.org/10.35814/coverage.v10i2.1377.
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This article focuses on the engagement conducted through Facebook fan page. Analyzing responses of customer to the postings made by corporate user will give input on what need to be put in attention and revise to meet the need of customers. The need to put more attention on details of social media feature becomes essentials in advancing engagement through –in this case- Facebook. The aim of the study is to identify highest engagement items in Facebook feature by examining Telkomsel Facebook page postings as a case. The data is content in Telkomsel Facebook. Data collection used Facebook-graph data mining technique. Media content analysis method conducted to analyze all the data using engagement concept. The study found 7 items that have the highest engagement rate on Facebook, which are: visual indicator in the form of video (1), 200 characters on Facebook texts (2), information with corporate brand, Telkomsel, on it (3); entertainment content (4), a call to action message to engage fans, such as quiz (5); high engagement happened in weekdays (6), to the multiple postings (7), and posting hours is in peak hours (8.00 – 17.00).
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Wu, Kuixian, Jian Gao, Rong Chen, and Xianji Cui. "Vertex Selection Heuristics in Branch-and-Bound Algorithms for the Maximum k-Plex Problem." International Journal on Artificial Intelligence Tools 28, no. 05 (August 2019): 1950015. http://dx.doi.org/10.1142/s0218213019500155.
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As a relaxation of clique in graph theory, k-plex is a powerful tool for analyzing social networks and identifying cohesive structures in graphs. Recently, more and more researchers have concentrated on the algorithms for the maximum k-plex problem. Among those algorithms, a branch-and-bound algorithm proposed very recently shows a good performance on solving large sparse graphs, but does not work well on social networks. In this paper, we propose two novel vertex selection heuristic strategies for branching. The first one employs historical information of vertex reduction, and the second one is a combination of the first heuristic and the degree-based approach. Intensive experiments on Facebook benchmark show that the algorithm combining our heuristics outperforms the state-of-the-art algorithms.
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Pfeiffer, Joseph, and Jennifer Neville. "Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 590–93. http://dx.doi.org/10.1609/icwsm.v5i1.14187.
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Much of the past work in network analysis has focused on analyzing discrete graphs, where binary edges represent the “presence” or “absence” of a relationship. Since traditional network measures (e.g., betweenness centrality) assume a discrete link structure, data about complex systems must be transformed to this representation before calculating network properties. In many domains where there may be uncertainty about the relationship structure, this transformation to a discrete representation will result in a lose of information. In order to represent and reason with network uncertainty, we move beyond the discrete graph framework and develop social network measures based on a probabilistic graph representation. More specifically, we develop measures of path length, betweenness centrality, and clustering coefficient— one set based on sampling and one based on probabilistic paths. We evaluate our methods on two real-world networks, Enron and Facebook, showing that our proposed methods more accurately capture salient effects without being susceptible to local noise.
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CRAMPES, MICHEL, and MICHEL PLANTIÉ. "A UNIFIED COMMUNITY DETECTION, VISUALIZATION AND ANALYSIS METHOD." Advances in Complex Systems 17, no. 01 (February 2014): 1450001. http://dx.doi.org/10.1142/s0219525914500015.
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With the widespread social networks on the Internet, community detection in social graphs has recently become an important research domain. Interest was initially limited to unipartite graph inputs and partitioned community outputs. More recently, bipartite graphs, directed graphs and overlapping communities have all been investigated. Few contributions however have encompassed all three types of graphs simultaneously. In this paper, we present a method that unifies community detection for these three types of graphs while at the same time it merges partitioned and overlapping communities. Moreover, the results are visualized in a way that allows for analysis and semantic interpretation. For validation purposes this method is experimented on some well-known simple benchmarks and then applied to real data: photos and tags in Facebook and Human Brain Tractography data. This last application leads to the possibility of applying community detection methods to other fields such as data analysis with original enhanced performances.
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Jain, Amita, Sunny Rai, Ankita Manaktala, and Lokender Sarna. "Analysing Asymmetrical Associations using Fuzzy Graph and Discovering Hidden Connections in Facebook." Global Journal of Enterprise Information System 9, no. 1 (May 5, 2017): 1. http://dx.doi.org/10.18311/gjeis/2017/15687.
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The fuzzy graph theory to analyse the relationship strength in Social Networks has gain significant potential in last few years and has seen applications in areas like Link Prediction, calculating Reciprocity, discovering central nodes etc. In this paper, we propose a framework to analyse and quantify the degree of strength of asymmetric relationships and predict hidden links in social networks using fuzzy logic. Till now, the work in fuzzy social relational networks has been limited to symmetric relationships. However, in this paper, we consider the scenario of asymmetric relations. The proposed approach is for web 2.0 application <em>Facebook</em>. Our contribution is three fold. First, the measurement of the strength of asymmetric relationship between nodes on the basis of social interaction using the concept of fuzzy graph. Second, a hybrid approach for prediction of missing links between two nodes on the basis of similarity of attributes of user profiles such as demographic, topology and network transactional data. Third, we perform fuzzy granular computing on attribute ‘strength of relationship’ and categorise into four granules namely <em>{socially close friends, socially near friends, socially far friends, socially very far friends}</em> based on the results of supervised learning conducted over dataset. Similarly, actual outcome for predicted links is categorised into three granules namely <em>Accept, Not accept and May be.</em> The proposed approach has predicted relationship strength with mean absolute error of 9.26% whereas the proposed approach for Link prediction has provided 64% correct predictions.
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Humski, Luka, Damir Pintar, and Mihaela Vranic. "Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation." IEEE Access 7 (2019): 6622–36. http://dx.doi.org/10.1109/access.2018.2886468.
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Freire, Ana, Matteo Manca, Diego Saez-Trumper, David Laniado, Ilaria Bordino, Francesco Gullo, and Andreas Kaltenbrunner. "Graph-Based Breaking News Detection on Wikipedia." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 2 (August 4, 2021): 41–42. http://dx.doi.org/10.1609/icwsm.v10i2.14830.
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Event detection in social media usually exploits information from social-networking platforms, such as Twitter or Facebook. However, previous research has suggested that Wikipedia constitutes a valuable source of information for the task of detecting breaking news. In this work we adapt a graph-based algorithm to the Wikipedia context, and compare it to the state-of-the-art Wikipedia real-time monitoring method. The main idea behind the proposed method is to extract breaking news by looking at unusual activity in the Wikipedia edit stream. We assess the performance of the two competing algorithms by measuring the percentage of true events correctly identified. Results show that the proposed graph-based method achieves better accuracy and coverage.
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Rozgonjuk, Dmitri, Cornelia Sindermann, Jon D. Elhai, Alexander P. Christensen, and Christian Montag. "Associations between symptoms of problematic smartphone, Facebook, WhatsApp, and Instagram use: An item-level exploratory graph analysis perspective." Journal of Behavioral Addictions 9, no. 3 (October 12, 2020): 686–97. http://dx.doi.org/10.1556/2006.2020.00036.
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AbstractBackground and aimsStudies have demonstrated associations between both problematic smartphone and social networks use with everyday life adversities. However, examination of associations between problematic smartphone use (PSU) and problematic use of specific social networking platforms, especially on item-level data, has received relatively little attention. Therefore, the aim of the current study was to explore how items of problematic smartphone, Facebook, WhatsApp, and Instagram use are associated.Methods949 German-speaking adults participated in a web survey study. The participants were queried about their socio-demographics as well as levels of problematic smartphone, Facebook, WhatsApp, and Instagram use. In addition to bivariate correlation analysis, exploratory graph analysis (EGA), a type of network analysis, was conducted.ResultsThe results showed that while problematic Facebook and Instagram use seem to be distinct phenomena, problematic smartphone and WhatsApp use were heavily intertwined. Furthermore, the only cross-platform symptom observed was the extent of reported pain in wrists and neck due to digital technology use. The EGA network models showed very good stability in bootstrap analyses.Discussion and conclusionsIn general, the results of this study suggest that while Instagram and Facebook use may potentially constitute distinct problematic behaviors, problematic smartphone/WhatsApp use scales may be measuring highly similar or even the same construct.
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Papadopoulou, Olga, Themistoklis Makedas, Lazaros Apostolidis, Francesco Poldi, Symeon Papadopoulos, and Ioannis Kompatsiaris. "MeVer NetworkX: Network Analysis and Visualization for Tracing Disinformation." Future Internet 14, no. 5 (May 10, 2022): 147. http://dx.doi.org/10.3390/fi14050147.
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The proliferation of online news, especially during the “infodemic” that emerged along with the COVID-19 pandemic, has rapidly increased the risk of and, more importantly, the volume of online misinformation. Online Social Networks (OSNs), such as Facebook, Twitter, and YouTube, serve as fertile ground for disseminating misinformation, making the need for tools for analyzing the social web and gaining insights into communities that drive misinformation online vital. We introduce the MeVer NetworkX analysis and visualization tool, which helps users delve into social media conversations, helps users gain insights about how information propagates, and provides intuition about communities formed via interactions. The contributions of our tool lie in easy navigation through a multitude of features that provide helpful insights about the account behaviors and information propagation, provide the support of Twitter, Facebook, and Telegram graphs, and provide the modularity to integrate more platforms. The tool also provides features that highlight suspicious accounts in a graph that a user should investigate further. We collected four Twitter datasets related to COVID-19 disinformation to present the tool’s functionalities and evaluate its effectiveness.
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Elmatsani, Huda M. "Desain Sistem Informasi Kehumasan Terintegrasi Situs Media Sosial." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 5, no. 1 (April 22, 2019): 24. http://dx.doi.org/10.26418/jp.v5i1.31164.
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Media sosial telah memasyarakat. Kehumasan yang mempunyai tugas berkomunikasi dengan masyarakat, mau tidak mau harus juga menggunakan media sosial sebagai salah satu saluran untuk berkomunikasi. Namun, di sisi lain terdapat kendala ketika harus mengumpulkan dan mengelola tanggapan balik dari masyarakat yang bersumber dari beragam media sosial. Penelitian ini mengembangkan sebuah desain sistem informasi kehumasan (SIK) yang terintegrasi dengan Facebook. Tujuannya agar SIK dapat mengelola informasi yang akan dipublikasikan ke medsos serta dapat mengevaluasi dan menanggapi balik setiap umpan balik sehingga hubungan dengan masyarakat dapat terjalin lebih baik. Desain SIK dibuat dengan dengan model arsitektur MVC dengan mengintegrasikan CodeIgniter dan Graph API, sedangkan pengembangan aplikasi dan pengu-jiannya menggunakan metode prototyping dan UML. Hasil pengujian menunjukkan SIK dapat mengelola konten yang dipublikasikan ke Facebook dan dapat mengelola konten umpan balik yang diperoleh dari Facebook.
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Nip, Joyce Y. M., and Yu-Chung Cheng. "Assessing the Impact of Digital Alternative News Media in a Hybrid News Environment: Cases from Taiwan and Hong Kong." Journalism and Media 3, no. 3 (September 17, 2022): 568–93. http://dx.doi.org/10.3390/journalmedia3030039.
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As consumption of mainstream news media declines and alternative news media proliferates, in this paper, we seek to assess the impact of digital alternative news media (DANM) in relation to mainstream news media (MNM). We examine the range of DANM, especially public Facebook pages, related to two large-scale social movements neighbouring mainland China as case studies of social movement media exerting maximalist effects. The assessment relies on academic sources, archival materials, descriptive social media metrics, and an original analysis of external content shared on public Facebook pages and groups using data collected from the Facebook Graph API. A six-dimensional scheme is proposed to guide the assessment. Sorting through and piecing together multiple sources, we arrive at a multi-faceted description, comparison, and analysis of the impact of DANM during two social movements.
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Masdiyasa, I. Gede Susrama, Gideon Setya Budiwitjaksono, Hafidz Amarul M, Ilham Ade Widya Sampurno, and Ni Made Ika Marini Mandenni. "Graph-QL Responsibility Analysis at Integrated Competency Certification Test System Base on Web Service." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 11, no. 2 (October 9, 2020): 114. http://dx.doi.org/10.24843/lkjiti.2020.v11.i02.p05.
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Graph-QL (Query Language) is a new concept in the Application Programming Interface (API). Graph-QL was developed by Facebook which is implemented on the server-side. Although it is a query language, Graph-QL is not directly related to the database, in other words, Graph-QL is not limited to certain databases, either SQL or NoSQL. The position of Graph-QL is on the client and server-side that access an API. One of the objectives of developing this query language is to facilitate data communication between the backend and frontend / mobile applications. For this reason, this paper will examine the responsibility of Graph-QL in terms of response time and response size in the development of an integrated competency certification test system based on web service and compared with efficiency and flexibility using the REST API. From the test results, it was found that Graph-QL provided some advantages compare to REST API. It give more flexibility for the clients to access the data and solve the most typical problem that was over or under fetching cause by fixed data given by REST API endpoints.
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Wang, Ruiheng, Hongliang Zhu, Lu Wang, Zhaoyun Chen, Mingcheng Gao, and Yang Xin. "User Identity Linkage Across Social Networks by Heterogeneous Graph Attention Network Modeling." Applied Sciences 10, no. 16 (August 7, 2020): 5478. http://dx.doi.org/10.3390/app10165478.
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Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.
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Gahardina, Ayu, and Ilman Zuhri Yadi. "Analisis Graph Clustering Terhadap User Behavior Di Official Account Facebook Universitas Bina Darma Palembang." Journal of Computer and Information Systems Ampera 1, no. 2 (May 25, 2020): 63–76. http://dx.doi.org/10.51519/journalcisa.v1i2.35.
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At a time when the times are increasing, it cannot be denied that almost some people use social media as a means of communication. Not only is it a means of communication, even social media is a place to show their habits. Without realizing it, the more information that is spread on social media regarding self-information, it will make information that can benefit others. User Behavior Analytics as defined by Gartner is a cybersecurity process of detection of insider threats, targeted attacks and financial fraud. The UBA solution looks at patterns of human behaviour and then applies algorithms and statistical analysis to detect meaningful anomalies of those patterns. Gephi is an open-source network visualization platform that can be used to analyze various cases using graph visualization. One of these analyzes can be done by using data that has been scraped from social media, Facebook, social media, Bina Darma University to get data visualization of the cases being tested. From these results, the graph clustering process is carried out in Gephi to obtain data clusters. The results will then be analyzed and identified so that they can become material for information about social media user behaviour.
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Bansal, Neetika, Vishal Goyal, and Simpel Rani. "Experimenting Language Identification for Sentiment Analysis of English Punjabi Code Mixed Social Media Text." International Journal of E-Adoption 12, no. 1 (January 2020): 52–62. http://dx.doi.org/10.4018/ijea.2020010105.
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People do not always use Unicode, rather, they mix multiple languages. The processing of codemixed data becomes challenging due to the linguistic complexities. The noisy text increases the complexities of language identification. The dataset used in this article contains Facebook and Twitter messages collected through Facebook graph API and twitter API. The annotated English Punjabi code mixed dataset has been trained using a pipeline Dictionary Vectorizer, N-gram approach with some features. Furthermore, classifiers used are Logistic Regression, Decision Tree Classifier and Gaussian Naïve Bayes are used to perform language identification at word level. The results show that Logistic Regression performs best with an accuracy of 86.63 with an F-1 measure of 0.88. The success of machine learning approaches depends on the quality of labeled corpora.
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Fattal, Alex. "Facebook: Corporate Hackers, a Billion Users, and the Geo-politics of the "Social Graph"." Anthropological Quarterly 85, no. 3 (2012): 927–55. http://dx.doi.org/10.1353/anq.2012.0051.
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Syn, Sue Yeon. "Health information communication during a pandemic crisis: analysis of CDC Facebook Page during COVID-19." Online Information Review 45, no. 4 (June 1, 2021): 672–86. http://dx.doi.org/10.1108/oir-09-2020-0416.
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PurposeThis study investigates the Centers for Disease Control and Prevention (CDC) Facebook Page to examine what kinds of information is shared to public using Facebook and how Facebook users share and engage with information during a health crisis situation with a case of the COVID-19 pandemic.Design/methodology/approachUsing Facebook Graph API, CDC's Facebook Page posts and users' engagement and reactions for six months from January to June 2020 were collected and analyzed. The posts were categorized into five categories. Users' engagement and reactions include share, comment, like, love, haha, wow, sad and angry.FindingsThe findings show that the type of posts have significant association with COVID-19 situation and the level of users' engagement and reactions differs significantly when COVID-19 related information is shared. The findings show that users become more active during health emergency situation. The results provided an insight into how different types of posts gain users' attention and motivation to interact.Originality/valueThis study investigates the use of social media during a national health crisis situation. While literature provides the use of social media during emergency and crisis cases, as health crisis situation is unique in that the boundary of time and location as well as people's daily life, the findings of this study provide an insight into how health authorities could communicate with the public during a health crisis situation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0416
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Atiqah Sia Abdullah, Nur, and Hamizah Binti Anuar. "Review of Data Visualization for Social Media Postings." International Journal of Engineering & Technology 7, no. 4.38 (December 3, 2018): 939. http://dx.doi.org/10.14419/ijet.v7i4.38.27613.
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Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.
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Song, Yi, Xuesong Lu, Sadegh Nobari, Stéphane Bressan, and Panagiotis Karras. "On the Privacy and Utility of Anonymized Social Networks." International Journal of Adaptive, Resilient and Autonomic Systems 4, no. 2 (April 2013): 1–34. http://dx.doi.org/10.4018/jaras.2013040101.
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One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.
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Bukhari, Sarah, Suraya Hamid, Sri Devi Ravana, Sherah Kurnia, Shanton Chang, Azah Anir Norman, and Norjihan Abdul Ghani. "The Use of Facebook by International Students for Information-seeking in Malaysia: A Social Network Analysis." Libri 70, no. 3 (September 25, 2020): 251–68. http://dx.doi.org/10.1515/libri-2019-0033.
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AbstractInternational students are valuable resources in higher education but they often face challenges due to lack of social engagement and unfamiliarity with the new social context. The use of social network sites (SNS) such as Facebook has the potential to support international students, but limited studies have examined the actual information seeking behaviour of this group of students. Therefore, this study aims to investigate the use of Facebook groups as an example of an SNS by international students for information seeking purposes. Data were downloaded from the Facebook group of international students that belong to a public university in Malaysia, and the social network analysis technique was used to analyse the data. The result of the network graph metrics showed that 25% international students exhibit a high frequency of interaction, whereas 75% of students present low interaction. Meanwhile, the result of the vertex text attribute method identified three types of information exchange, as follows: 1) information need; 2) information source; and 3) general information during the interaction of international students. The information needs of international students differ before and after arriving in Malaysia. Thus, Facebook groups provide a platform for international students to seek information, gain knowledge, remain updated with university news, make decisions and solve problems. This study offers important implications to research and practice related to the use of social network sites to support international students.
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Adityo, R. Dimas, Tri Yogi Rizqi, and Mas Nurul Hamidah. "Android Based Hate Speech Search Applications Using TF-IDF Algorithm and Vector Space Models." JEECS (Journal of Electrical Engineering and Computer Sciences) 4, no. 1 (June 28, 2019): 611–24. http://dx.doi.org/10.54732/jeecs.v4i1.120.
Full textAbstract:
The freedom in the use of social media becomes a common means in voicing opinions and expressions byissuing words or phrases of blasphemy (Hate Speech) in social media like Facebook. Hate speech and blasphemouswords are easily spread across social and general media not found that have transgressed the limits and can trace theunrest. To find out what the users of social media especially Facebook we have issued words or phrases of blasphemyon the basis of this report occurred. The study was conducted by using TF-IDF weighting algorithm and VSM (VectorSpace Model) calculation, while the data used was the 30 users post using Graph API. This research resulted in searchwith recall value: 2/2 = 1 which means the result of the relevant document and the precision value of the search resultis 2/13 = 0,154 which means result also raises irrelevant search result. Percentage of hate speech equivalence valuewas 43%.
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Le, Van-Vang, Phuong Nguyen Huy Pham, Tran Kim Toai, and Vaclav Snasel. "An approach of anchor link prediction using graph attention mechanism." Bulletin of Electrical Engineering and Informatics 11, no. 5 (October 1, 2022): 2895–902. http://dx.doi.org/10.11591/eei.v11i5.4274.
Full textAbstract:
Nowadays social networks such as Twitter, LinkedIn, and Facebook are a popular and necessary platform. It is considered a miniature of an actual social network because of its advantages in connecting and sharing information between users. The analysis of data on online social networks has become a field that has attracted a lot of attention from the research community and anchor link prediction is one of the main research directions in this field. Depending on demand, a user can simultaneously participate in many different online social networks, anchor link prediction is a kind of task that determines the identity of a user on many different social networks. In this article, we proposed an algorithm that determines missing/future anchor links between users from two different online social networks. Our algorithm utilizes the graph attention technique to represent the source and target network into the low-dimension embedding spaces, we then apply the canonical correlation analysis to recline their embeddings into same latent spaces for final prediction.
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Zhou, Chao, and Tao Zhang. "High Performance Graph Data Imputation on Multiple GPUs." Future Internet 13, no. 2 (January 31, 2021): 36. http://dx.doi.org/10.3390/fi13020036.
Full textAbstract:
In real applications, massive data with graph structures are often incomplete due to various restrictions. Therefore, graph data imputation algorithms have been widely used in the fields of social networks, sensor networks, and MRI to solve the graph data completion problem. To keep the data relevant, a data structure is represented by a graph-tensor, in which each matrix is the vertex value of a weighted graph. The convolutional imputation algorithm has been proposed to solve the low-rank graph-tensor completion problem that some data matrices are entirely unobserved. However, this data imputation algorithm has limited application scope because it is compute-intensive and low-performance on CPU. In this paper, we propose a scheme to perform the convolutional imputation algorithm with higher time performance on GPUs (Graphics Processing Units) by exploiting multi-core GPUs of CUDA architecture. We propose optimization strategies to achieve coalesced memory access for graph Fourier transform (GFT) computation and improve the utilization of GPU SM resources for singular value decomposition (SVD) computation. Furthermore, we design a scheme to extend the GPU-optimized implementation to multiple GPUs for large-scale computing. Experimental results show that the GPU implementation is both fast and accurate. On synthetic data of varying sizes, the GPU-optimized implementation running on a single Quadro RTX6000 GPU achieves up to 60.50× speedups over the GPU-baseline implementation. The multi-GPU implementation achieves up to 1.81× speedups on two GPUs versus the GPU-optimized implementation on a single GPU. On the ego-Facebook dataset, the GPU-optimized implementation achieves up to 77.88× speedups over the GPU-baseline implementation. Meanwhile, the GPU implementation and the CPU implementation achieve similar, low recovery errors.
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Santia, Giovanni, and Jake Williams. "BuzzFace: A News Veracity Dataset with Facebook User Commentary and Egos." Proceedings of the International AAAI Conference on Web and Social Media 12, no. 1 (June 15, 2018): 531–40. http://dx.doi.org/10.1609/icwsm.v12i1.14985.
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Veracity assessment of news and social bot detection have become two of the most pressing issues for social media platforms, yet current gold-standard data are limited. This paper presents a leap forward in the development of a sizeable and feature rich gold-standard dataset. The dataset was built by using a collection of news items posted to Facebook by nine news outlets during September 2016, which were annotated for veracity by BuzzFeed. These articles were refined beyond binary annotation to the four categories: mostly true, mostly false, mixture of true and false, and no factual content. Our contribution integrates data on Facebook comments and reactions publicly available on the platform’s Graph API, and provides tailored tools for accessing news article web content. The features of the accessed articles include body text, images, links, Facebook plugin comments, Disqus plugin comments, and embedded tweets. Embedded tweets provide a potent possible avenue for expansion across social media platforms. Upon development, this utility yielded over 1.6 million text items, making it over 400 times larger than the current gold-standard. The resulting dataset—BuzzFace—is presently the most extensive created, and allows for more robust machine learning applications to news veracity assessment and social bot detection than ever before.
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P, Srilatha, and Manjula R. "A Weighted path based Link Prediction in Social Networks using Bounded Length of Separation between Nodes." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 274. http://dx.doi.org/10.14419/ijet.v7i4.10.20911.
Full textAbstract:
The problem of link prediction in online social networks like facebook, myspace, Hi5 and in other domains like biological network of molecules, gene network to model disease have became very popular because of the structural connections and relationships among the entities. The classical methods of link prediction based on the topological structure of the graph exploit all different paths of the network which are being computationally expensive for large size of networks. In this paper, incorporating the small world phenomenon, the proposed algorithm traverses all the paths of bounded length by considering clustering information and the connection pattern of the edges as weights on the edges in the graph. As a result, the proposed algorithm will be able to predict accurately than the existing link prediction algorithms. Our analysis and experiment on real world networks shows that our algorithm outperforms other approaches in terms of time complexity and the prediction accuracy.
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Forouzandeh, Saman, Amir Sheikhahmadi, Atae Rezaei Aghdam, and Shuxiang Xu. "New centrality measure for nodes based on user social status and behavior on Facebook." International Journal of Web Information Systems 14, no. 2 (June 18, 2018): 158–76. http://dx.doi.org/10.1108/ijwis-07-2017-0053.
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Purpose This paper aims to analyze the role of influential nodes on other users on Facebook social media sites by social and behavioral characteristics of users. Hence, a new centrality for user is defined, applying susceptible-infected recovered (SIR) model to identify influence of users. Results show that the combination of behavioral and social characteristics would be determined the most influential users that influence majority of nodes on social networks. Design/methodology/approach In this paper, the authors define a new centrality for users, considering node status and behaviors. Thus, this node has a high level of influence. Node social status includes node degree, clustering coefficient and average neighbors’ node, and social status of node refers to user activities on Facebook social media website such as sending posts and receiving likes from other users. According to social status and user activity, the new centrality is defined. Finally, through the SIR model, the authors explore infection power of nodes and their influences of other node in the network. Findings Results show that the proposed centrality is more effective than other centrality approaches, infecting more nodes in social network. Another significant point in this research is that users who have high social status and activities on Facebook are more influential than users who have only high social status on the Facebook social media. Originality/value The influence of user on others in social media includes two key factors. The first factor is user social status such as node degree and clustering coefficient in social media graph and the second factor is related to user social activities in social media sites. Most centralities focused on node social status without considering node behavior. This paper analyzes the role of influential nodes on other users on Facebook social media site by social and behavioral characteristics of users.
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Fatima Kiran. "HEALTH INFORMATION SHARING ON FACEBOOK: AN EXPLORATORY STUDY ON COVID-19." Global Journal for Management and Administrative Sciences 3, no. 1 (April 30, 2022): 43–54. http://dx.doi.org/10.46568/gjmas.v3i1.101.
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Social media is the most crucial part of our daily lives. Today the internet and World Wide Web are being used to influence people around the globe. However, the information available on social media struggles to maintain its credibility because anyone may upload content. Currently, it has been observed that Facebook being the biggest social media platform plays a significant role in the dissemination of information about the COVID-19 pandemic. The objective of this study was to investigate the information about COVID-19 on Pakistan's biggest page dedicated to COVID-19 known as Corona Recovered Warriors. The researcher conducted a qualitative and quantitative Facebook content analysis. The content was restricted to COVID-19 information available on the Corona Recovered Warriors Facebook page. The posts from June, September, and December were selected to analyze the change in themes of information over time. In Pakistan, in June, the highest number of daily COVID-19 positive cases are reported, September because of the ease in lockdown and the lowest number of COVID-19 daily cases, and then December when the graph of COVID-19 positive case again rises. A total of 28381 posts were analyzed in three months, and the focus of the posts was on Experience sharing (n=7934), Seeking or clarifying personal status (n=8419), Raising awareness on COVID-19 (n=2614), Support for patient and caregiver (n= 2597), and Product and service promotion (n=3341). A significant link between the number of posts on the Facebook page and the crucial and easy months related to COVID-19 positive cases has also been detected. COVID-19-positive patients and their relatives or family members share their health information widely with other Facebook users, also highlighting the need for collaboration between healthcare providers and media experts to design appropriate interventions for responding to pandemic diseases.
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Guo, Huihui, Li Yang, and Zeyu Liu. "UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning." Wireless Communications and Mobile Computing 2021 (July 9, 2021): 1–17. http://dx.doi.org/10.1155/2021/4431416.
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Social and information networks such as Facebook, Twitter, and Weibo have become the main social platforms for the public to share and exchange information, where we can easily access friends’ activities and in turn be influenced by them. Consequently, the analysis and modeling of user retweet behavior prediction have an important application value, such as information dissemination, public opinion monitoring, and product recommendation. Most of the existing solutions for user retweeting behavior prediction are usually based on network topology maps of information dissemination or designing various handcrafted rules to extract user-specific and network-specific features. However, these methods are very complex or heavily dependent on the knowledge of domain experts. Inspired by the successful use of neural networks in representation learning, we design a framework, UserRBPM, to explore potential driving factors and predictable signals in user retweet behavior. We use the graph embedding technology to extract the structural attributes of the ego network, consider the drivers of social influence from the spatial and temporal levels, and use graph convolutional networks and the graph attention mechanism to learn its potential social representation and predictive signals. Experimental results show that our proposed UserRBPM framework can significantly improve prediction performance and express social influence better than traditional feature engineering-based approaches.
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Tomeo, Paolo, Ignacio Fernández-Tobías, Iván Cantador, and Tommaso Di Noia. "Addressing the Cold Start with Positive-Only Feedback Through Semantic-Based Recommendations." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 57–78. http://dx.doi.org/10.1142/s0218488517400116.
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Recommender systems aim to provide users with accurate item suggestions in a personalized fashion, but struggle in the case of cold start users, for whom there is a scarcity of preference data. User preferences can be either explicitly stated by the users — often by means of ratings —, or implicitly acquired by a system — for instance by mining text reviews, search queries, and purchase records. Recommendation methods have been mostly designed to deal with numerical ratings. However, real scenarios with user preferences expressed in the form of binary and unary (positive-only) feedback, e.g. the thumbs up/down in YouTube, and the likes in Facebook, are increasingly popular, and make the user cold start problem even more challenging. To address the cold start with positive-only feedback situations, we propose to exploit data additional to user preferences by means of specialized hybrid recommendation methods. In particular, we investigate a number of graph-based and matrix factorization recommendation models that jointly exploit user preferences and item semantic metadata automatically extracted from the well-known knowledge graph of DBpedia. Following a rigorous evaluation methodology for cold start, we empirically compare the above hybrid recommendation models on a Facebook dataset containing users likes for items in three different domains, namely books, movies and music. The achieved experimental results show that the semantics-aware hybrid approaches we propose outperform content-based and collaborative filtering baselines. In addition to recommendation accuracy, in our evaluation we also consider individual and aggregate diversity of recommendations as key quality factors in the users’ satisfaction.
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Dhurandhar, A., and J. Wang. "Single Network Relational Transductive Learning." Journal of Artificial Intelligence Research 48 (November 30, 2013): 813–39. http://dx.doi.org/10.1613/jair.4068.
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Relational classification on a single connected network has been of particular interest in the machine learning and data mining communities in the last decade or so. This is mainly due to the explosion in popularity of social networking sites such as Facebook, LinkedIn and Google+ amongst others. In statistical relational learning, many techniques have been developed to address this problem, where we have a connected unweighted homogeneous/heterogeneous graph that is partially labeled and the goal is to propagate the labels to the unlabeled nodes. In this paper, we provide a different perspective by enabling the effective use of graph transduction techniques for this problem. We thus exploit the strengths of this class of methods for relational learning problems. We accomplish this by providing a simple procedure for constructing a weight matrix that serves as input to a rich class of graph transduction techniques. Our procedure has multiple desirable properties. For example, the weights it assigns to edges between unlabeled nodes naturally relate to a measure of association commonly used in statistics, namely the Gamma test statistic. We further portray the efficacy of our approach on synthetic as well as real data, by comparing it with state-of-the-art relational learning algorithms, and graph transduction techniques with an adjacency matrix or a real valued weight matrix computed using available attributes as input. In these experiments we see that our approach consistently outperforms other approaches when the graph is sparsely labeled, and remains competitive with the best when the proportion of known labels increases.
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Paryono, Yani. "PENGEMBANGAN BAHAN BELAJAR KOSAKATA BAHASA INDONESIA BERBASIS PENDIDIKAN NILAI-NILAI PANCASILA DI FACEBOOK." Jurnal Kwangsan 5, no. 2 (December 17, 2017): 13. http://dx.doi.org/10.31800/jkwangsan-jtp.v5n2.p13.
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Good Indonesian language learning materials are not only taken from textbooks but they can also be taken from various sources such as newspaper, outdoor media, nature, television, and social media. The research entitled ‘The Development of Indonesian Language Vocabulary Learning Materials Based on the Education of Pancasila Values on Facebook’ aims at producing Indonesian learning materials based on the education of Pancasila values. The problem discussed in this article is that there hasn’t been any Indonesian vocabulary learning material based on the education of Pancasila values developed by teachers and published on Facebook. The method used in this research is a Four-D Model consisting of some stages, such as: defining, planning, developing, and presenting on a seminar. This research involves an expert of Indonesian language education, six Indonesian language teachers, and twenty Senior High School students. Indonesian language learning materials on Facebook can be used to instill the values of Pancasila. The development of these learning materials is based on the principles of relevance, consistency, sufficiency properness of content, language, presentation, and graph. It has been proven through this research that the Indonesian subject learning achievement of Senior Secondary students through the use of facebook containing Pancasila values as learning content is higher compared to those students using different learning materials. AbstrakBahan belajar bahasa Indonesia yang baik tidak harus bersumber pada buku-buku pelajaran saja, tetapi dapat juga memanfaatkan berbagai sumber, seperti: media cetak koran, media luar ruang, alam sekitar, televisi, dan media sosial. Penelitian dengan judul Pengembangan Bahan Belajar Kosakata Bahasa Indonesia Berbasis Pendidikan Nilai-Nilai Pancasila di Facebook bertujuan untuk menghasilkan bahan belajar bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dapat diakses melalui facebook. Masalah yang dibahas di dalam artikel ini adalah belum adanya materi pelajaran kosakata bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dikembangkan guru dan di-publish di facebook. Metode penelitian yang digunakan adalah Four-D Model yang meliputi tahap pendefinisian, perancangan, pengembangan,dan tahap pendiseminasian. Penelitian ini melibatkan seorang ahli pendidikan bahasa Indonesia, 6 guru bahasa Indonesia, dan 20 siswa SMA. Bahan belajar bahasa Indonesia yang disajikan di media sosial facebook dapat dimanfaatkan untuk menanamkan nilai-nila Pancasila. Bahan belajar bahasa Indonesia ini dikembangkan dengan memperhatikan prinsip relevansi, konsistensi, ketercukupan, kelayakan isi, kebahasaan, penyajian, dan kegrafikaan. Hasil belajar bahasa Indonesia siswa SMA melalui bahan belajar yang menggunakan kosakata bahasa Indonesia berbasis nilainilai Pancasila di media sosial facebook terbukti lebih tinggi dan lebih baik dibandingkan hasil belajar siswa yang menggunakan bahan belajar yang tidak sama.
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Paryono, Yani. "PENGEMBANGAN BAHAN BELAJAR KOSAKATA BAHASA INDONESIA BERBASIS PENDIDIKAN NILAI-NILAI PANCASILA DI FACEBOOK." Jurnal Kwangsan 5, no. 2 (December 17, 2017): 115. http://dx.doi.org/10.31800/jtp.kw.v5n2.p115--127.
Full textAbstract:
Good Indonesian language learning materials are not only taken from textbooks but they can also be taken from various sources such as newspaper, outdoor media, nature, television, and social media. The research entitled ‘The Development of Indonesian Language Vocabulary Learning Materials Based on the Education of Pancasila Values on Facebook’ aims at producing Indonesian learning materials based on the education of Pancasila values. The problem discussed in this article is that there hasn’t been any Indonesian vocabulary learning material based on the education of Pancasila values developed by teachers and published on Facebook. The method used in this research is a Four-D Model consisting of some stages, such as: defining, planning, developing, and presenting on a seminar. This research involves an expert of Indonesian language education, six Indonesian language teachers, and twenty Senior High School students. Indonesian language learning materials on Facebook can be used to instill the values of Pancasila. The development of these learning materials is based on the principles of relevance, consistency, sufficiency properness of content, language, presentation, and graph. It has been proven through this research that the Indonesian subject learning achievement of Senior Secondary students through the use of facebook containing Pancasila values as learning content is higher compared to those students using different learning materials. AbstrakBahan belajar bahasa Indonesia yang baik tidak harus bersumber pada buku-buku pelajaran saja, tetapi dapat juga memanfaatkan berbagai sumber, seperti: media cetak koran, media luar ruang, alam sekitar, televisi, dan media sosial. Penelitian dengan judul Pengembangan Bahan Belajar Kosakata Bahasa Indonesia Berbasis Pendidikan Nilai-Nilai Pancasila di Facebook bertujuan untuk menghasilkan bahan belajar bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dapat diakses melalui facebook. Masalah yang dibahas di dalam artikel ini adalah belum adanya materi pelajaran kosakata bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dikembangkan guru dan di-publish di facebook. Metode penelitian yang digunakan adalah Four-D Model yang meliputi tahap pendefinisian, perancangan, pengembangan,dan tahap pendiseminasian. Penelitian ini melibatkan seorang ahli pendidikan bahasa Indonesia, 6 guru bahasa Indonesia, dan 20 siswa SMA. Bahan belajar bahasa Indonesia yang disajikan di media sosial facebook dapat dimanfaatkan untuk menanamkan nilai-nilai Pancasila. Bahan belajar bahasa Indonesia ini dikembangkan dengan memperhatikan prinsip relevansi, konsistensi, ketercukupan, kelayakan isi, kebahasaan, penyajian, dan kegrafikaan. Hasil belajar bahasa Indonesia siswa SMA melalui bahan belajar yang menggunakan kosakata bahasa Indonesia berbasis nilainilai Pancasila di media sosial facebook terbukti lebih tinggi dan lebih baik dibandingkan hasil belajar siswa yang menggunakan bahan belajar yang tidak sama.
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Paryono, Yani. "PENGEMBANGAN BAHAN BELAJAR KOSAKATA BAHASA INDONESIA BERBASIS PENDIDIKAN NILAI-NILAI PANCASILA DI FACEBOOK." Jurnal Kwangsan 5, no. 2 (December 17, 2017): 115. http://dx.doi.org/10.31800/jtpk.v5n2.p115--127.
Full textAbstract:
Good Indonesian language learning materials are not only taken from textbooks but they can also be taken from various sources such as newspaper, outdoor media, nature, television, and social media. The research entitled ‘The Development of Indonesian Language Vocabulary Learning Materials Based on the Education of Pancasila Values on Facebook’ aims at producing Indonesian learning materials based on the education of Pancasila values. The problem discussed in this article is that there hasn’t been any Indonesian vocabulary learning material based on the education of Pancasila values developed by teachers and published on Facebook. The method used in this research is a Four-D Model consisting of some stages, such as: defining, planning, developing, and presenting on a seminar. This research involves an expert of Indonesian language education, six Indonesian language teachers, and twenty Senior High School students. Indonesian language learning materials on Facebook can be used to instill the values of Pancasila. The development of these learning materials is based on the principles of relevance, consistency, sufficiency properness of content, language, presentation, and graph. It has been proven through this research that the Indonesian subject learning achievement of Senior Secondary students through the use of facebook containing Pancasila values as learning content is higher compared to those students using different learning materials. AbstrakBahan belajar bahasa Indonesia yang baik tidak harus bersumber pada buku-buku pelajaran saja, tetapi dapat juga memanfaatkan berbagai sumber, seperti: media cetak koran, media luar ruang, alam sekitar, televisi, dan media sosial. Penelitian dengan judul Pengembangan Bahan Belajar Kosakata Bahasa Indonesia Berbasis Pendidikan Nilai-Nilai Pancasila di Facebook bertujuan untuk menghasilkan bahan belajar bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dapat diakses melalui facebook. Masalah yang dibahas di dalam artikel ini adalah belum adanya materi pelajaran kosakata bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dikembangkan guru dan di-publish di facebook. Metode penelitian yang digunakan adalah Four-D Model yang meliputi tahap pendefinisian, perancangan, pengembangan,dan tahap pendiseminasian. Penelitian ini melibatkan seorang ahli pendidikan bahasa Indonesia, 6 guru bahasa Indonesia, dan 20 siswa SMA. Bahan belajar bahasa Indonesia yang disajikan di media sosial facebook dapat dimanfaatkan untuk menanamkan nilai-nila Pancasila. Bahan belajar bahasa Indonesia ini dikembangkan dengan memperhatikan prinsip relevansi, konsistensi, ketercukupan, kelayakan isi, kebahasaan, penyajian, dan kegrafikaan. Hasil belajar bahasa Indonesia siswa SMA melalui bahan belajar yang menggunakan kosakata bahasa Indonesia berbasis nilainilai Pancasila di media sosial facebook terbukti lebih tinggi dan lebih baik dibandingkan hasil belajar siswa yang menggunakan bahan belajar yang tidak sama.
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Paryono, Yani. "PENGEMBANGAN BAHAN BELAJAR KOSAKATA BAHASA INDONESIA BERBASIS PENDIDIKAN NILAI-NILAI PANCASILA DI FACEBOOK." Jurnal Kwangsan 5, no. 2 (December 17, 2017): 13. http://dx.doi.org/10.31800/jurnalkwangsan.v5i2.45.
Full textAbstract:
Good Indonesian language learning materials are not only taken from textbooks but they can also be taken from various sources such as newspaper, outdoor media, nature, television, and social media. The research entitled ‘The Development of Indonesian Language Vocabulary Learning Materials Based on the Education of Pancasila Values on Facebook’ aims at producing Indonesian learning materials based on the education of Pancasila values. The problem discussed in this article is that there hasn’t been any Indonesian vocabulary learning material based on the education of Pancasila values developed by teachers and published on Facebook. The method used in this research is a Four-D Model consisting of some stages, such as: defining, planning, developing, and presenting on a seminar. This research involves an expert of Indonesian language education, six Indonesian language teachers, and twenty Senior High School students. Indonesian language learning materials on Facebook can be used to instill the values of Pancasila. The development of these learning materials is based on the principles of relevance, consistency, sufficiency properness of content, language, presentation, and graph. It has been proven through this research that the Indonesian subject learning achievement of Senior Secondary students through the use of facebook containing Pancasila values as learning content is higher compared to those students using different learning materials. AbstrakBahan belajar bahasa Indonesia yang baik tidak harus bersumber pada buku-buku pelajaran saja, tetapi dapat juga memanfaatkan berbagai sumber, seperti: media cetak koran, media luar ruang, alam sekitar, televisi, dan media sosial. Penelitian dengan judul Pengembangan Bahan Belajar Kosakata Bahasa Indonesia Berbasis Pendidikan Nilai-Nilai Pancasila di Facebook bertujuan untuk menghasilkan bahan belajar bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dapat diakses melalui facebook. Masalah yang dibahas di dalam artikel ini adalah belum adanya materi pelajaran kosakata bahasa Indonesia berbasis pendidikan nilai-nilai Pancasila yang dikembangkan guru dan di-publish di facebook. Metode penelitian yang digunakan adalah Four-D Model yang meliputi tahap pendefinisian, perancangan, pengembangan,dan tahap pendiseminasian. Penelitian ini melibatkan seorang ahli pendidikan bahasa Indonesia, 6 guru bahasa Indonesia, dan 20 siswa SMA. Bahan belajar bahasa Indonesia yang disajikan di media sosial facebook dapat dimanfaatkan untuk menanamkan nilai-nila Pancasila. Bahan belajar bahasa Indonesia ini dikembangkan dengan memperhatikan prinsip relevansi, konsistensi, ketercukupan, kelayakan isi, kebahasaan, penyajian, dan kegrafikaan. Hasil belajar bahasa Indonesia siswa SMA melalui bahan belajar yang menggunakan kosakata bahasa Indonesia berbasis nilainilai Pancasila di media sosial facebook terbukti lebih tinggi dan lebih baik dibandingkan hasil belajar siswa yang menggunakan bahan belajar yang tidak sama.
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Fähnrich, Birte, Jens Vogelgesang, and Michael Scharkow. "Evaluating universities' strategic online communication: how do Shanghai Ranking's top 50 universities grow stakeholder engagement with Facebook posts?" Journal of Communication Management 24, no. 3 (May 10, 2020): 265–83. http://dx.doi.org/10.1108/jcom-06-2019-0090.
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PurposeThis study is dedicated to universities' strategic social media communication and focuses on the fan engagement triggered by Facebook postings. The study contributes to a growing body of knowledge that addresses the strategic communication of universities that have thus far hardly dealt with questions of resonance and evaluation of their social media messages.Design/methodology/approachUsing the Facebook Graph API, the authors collected posts from the official Facebook fan pages of the universities listed on Shanghai Ranking's Top 50 of 2015. Specifically, the authors retrieved all posts in a three-year range from October 2012 to September 2015. After downloading the Facebook posts, the authors used tools for automated content analysis to investigate the features of the post messages.FindingsOverall, the median number of likes per 10,000 fans was 4.6, while the number of comments (MD = 0.12) and shares (MD = 0.40) were considerably lower. The average Facebook Like Ratio of universities per 10,000 fans was 17.93%, the average Comment Ratio (CR) was 0.56% and the average Share Ratio (SR) was 2.82%. If we compare the average Like Ratios (17.93%) and Share Ratios (2.82%) of the universities with the respective Like Ratios (5.90%) and Share Ratios (0.45%) of global brands per 10,000 fans, we may find that universities are three times (likes) and six times (shares) as successful as are global brands in triggering engagement among their fan bases.Research limitations/implicationsThe content analysis was solely based on the publicly observable Facebook communication of the Top 50 Shanghai Ranking universities. Furthermore, the content analysis was limited to universities listed on the Shanghai Ranking's Top 50. Also, the Facebook posts have been sampled between 2012 and September 2015. Moreover, the authors solely focused on one social media channel (i.e., Facebook), which might restrict the generalizability of the study findings. The limitations notwithstanding, university communicators are invited to take advantage of the study's insights to become more successful in generating fan engagement.Practical implicationsFirst, posts published on the weekend generate significantly more engagement than those published on workdays. Second, the findings suggest that posts published in the evening generate more engagement than those published during other times of day. Third, research-related posts trigger a certain number of shares, but at the same time these posts tend to lower engagement with regard to liking and commenting.Originality/valueTo the authors’ best knowledge, the automated content analysis of 72,044 Facebook posts of universities listed in the Top 50 of the Shanghai Ranking is the first large scale longitudinal investigation of a social media channel of higher education institutions.
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Low, Piyada. "An Investigation of English Usage in Careers of Thai Graduates with a Business Administration Degree." Shanlax International Journal of Education 9, no. 1 (December 1, 2020): 27–32. http://dx.doi.org/10.34293/education.v9i1.3416.
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The research study aims to investigate English usage in careers of graduates with business administration degree in seven fields of study: Finance, Management, Marketing, International Business, Hotel and Tourism Management, Managerial Accounting, and Logistics Management. Google apps, Facebook, and LINE were the main instruments to collect the data. The questionnaire was created in a Google Form, then, was distributed and shared via Facebook, Facebook messenger, and LINE. Data were analyzed in the forms of graph, chart and percentages by using Google Data Studio. SPSS was also employed to interpret the 5 rating scale questions. The number of respondents was 221. 41.6% of the respondents were 22 years old and they were fresh graduates. Level of English usage in the workplace was high (M=3.57) especially reading skill (M=3.63). English speaking was mentioned as the most useful course at work. English for Specific Purposes (ESP) courses were mentioned in particular careers. Graduates in Managerial Accounting mentioned English for Accountant I and II while graduates in Hotel and Tourism Management mentioned Technical English for Hotel Business, Technical English for Tourism Business, and English for Food and Beverage Service. The most required training topic was English communication, followed by English speaking and English writing. The respondents also perceived that the English language was important for career advancement (M=3.92).
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Fakas, Georgios J., and Zhi Cai. "Object summaries for keyword search." Encyclopedia with Semantic Computing and Robotic Intelligence 02, no. 02 (November 14, 2018): 1750002. http://dx.doi.org/10.1142/s2529737617500022.
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The abundance and ubiquity of graphs (e.g., semantic knowledge graphs, such as Google’s knowledge graph, DBpedia; online social networks such as Google[Formula: see text], Facebook; bibliographic graphs such as DBLP, etc.) necessitates the effective and efficient search over them. Thus, we propose a novel keyword search paradigm, where the result of a search is an Object Summary (OS). More precisely, given a set of keywords that can identify a Data Subject (DS), our paradigm produces a set of OSs as results. An OS is a tree structure rooted at the DS node (i.e., a node containing the keywords) with surrounding nodes that summarize all data held on the graph about the DS. An OS can potentially be very large in size and therefore unfriendly for users who wish to view synoptic information about the data subject. Thus, we investigate the effective and efficient retrieval of concise and informative OS snippets (denoted as size-[Formula: see text] OSs). A size-[Formula: see text] OS is a partial OS containing [Formula: see text] nodes such that the summation of their importance scores results in the maximum possible total score. However, the set of nodes that maximize the total importance score may result in an uninformative size-[Formula: see text] OSs, as very important nodes may be repeated in it, dominating other representative information. In view of this limitation, we investigate the effective and efficient generation of two novel types of OS snippets, i.e., diverse and proportional size-[Formula: see text] OSs, denoted as DSize-[Formula: see text] and PSize-[Formula: see text] OSs. Namely, besides the importance of each node, we also consider its pairwise relevance (similarity) to the other nodes in the OS and the snippet. We conduct an extensive evaluation on two real graphs (DBLP and Google[Formula: see text]). We verify effectiveness by collecting user feedback, e.g., by asking DBLP authors (i.e., the DSs themselves) to evaluate our results. In addition, we verify the efficiency of our algorithms and evaluate quality of the snippets that they produce.
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Shami, Savera, Shazia Ismail Toor, and Ayesha Ashfaq. "Social Media and Strategic Communication: Uses and Preferences of the Politicians of Pakistan." Global Political Review V, no. III (September 30, 2020): 80–89. http://dx.doi.org/10.31703/gpr.2020(v-iii).08.
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Social media has got a major share in our personal as well as professional communication because of its easy accessibility and highly interactive nature. Facebook and Twitter can be used as important tool to mobilize groups to take some action (Shirky, 2008, p. 184). It has been observed that political leaders in Pakistan use social media to connect with the audience, but no research has been conducted in this regard so far. Therefore, the main objective of this study is to find which platform does the political actors of Pakistan prefer to disseminate information and in which medium they believe more. Furthermore, this study also looks into the popularity graph of the politicians, whether the use of social media has increased their popularity or not. To fulfil the objectives of the study, the survey was conducted among the members of national and provincial assemblies (2013-2018). With the help of findings, it was concluded that the majority of the politicians in Pakistan prefer Facebook over Twitter when they want to share any information, which is unlike the scenario around the globe.
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Nurkarim, Wahidya, and Arie Wahyu Wijayanto. "Revisiting Local Walking Based on Social Network Trust (LWSNT): Friends Recommendation Algorithm in Facebook Social Networks." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (January 4, 2022): 202–10. http://dx.doi.org/10.34123/icdsos.v2021i1.124.
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In the last decades, the internet penetration rate and online social network users have grown very fast. Online social network, such as Facebook, is a platform where one can find friends without having to meet face to face. A social network is represented by a large graph because it involves many participants. Hence, it is hard to find potential friends who have the same thoughts and interests. The Local Walking Based on Social Network Trust (LWSNT) algorithm is one of the popular algorithms for social friend recommendation. This study re-examines whether the correlation between attributes gives un-match ranks in different cases (cases with and without correlation). We assess the performance of LWSNT in Facebook networks under the supervised manner by comparing its F-score against similar methods. By using Kendall’s tau correlation, the results show that the correlation of attributes has no significant effect on the order of friend recommendations. In addition, the LWSNT performance is quite inferior against the Common Neighbors algorithm and Jaccard index.
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Arsić, Branko, Ljubiša Bojić, Ivan Milentijević, Petar Spalević, and Dejan Rančić. "SYMBOLS: SOFTWARE FOR SOCIAL NETWORK ANALYSIS." Facta Universitatis, Series: Automatic Control and Robotics 17, no. 3 (January 8, 2019): 205. http://dx.doi.org/10.22190/fuacr1803205a.
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The unique possibilities of the online social networks such as real-time data access, knowledge of users’ changing preferences and access to their statuses provide the possibility for innovation in the analysis of people’s behavior and opinions, when compared to classical offline methods. Literature review shows lack of studies about the use of public Facebook data in Serbia for the improvement of different product sale, political or promotional campaigns, recommender systems, etc. In this paper, we present the way how data from Facebook can be collected in order to gain insight into the individuals’ preferences and statuses, as well as their connection to a company's fan pages. In particular, we present data collection framework – Symbols – used for collecting individual specific data. The framework stores data into local database and involves a module for graph and content-based analysis of these data. The proposed framework for social network analysis can be used as a decision-making system in users’ preferences implementation thus creating a space for business improvements in various areas.
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Huber, André. "Facebook graph search for refined screen-based data collection in CMC: A pilot study for Fiji English." Discourse, Context & Media 10 (December 2015): 10–18. http://dx.doi.org/10.1016/j.dcm.2015.03.001.
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Zhao, Yuying, Yunfei Hu, Pingpeng Yuan, and Hai Jin. "Maximizing Influence Over Streaming Graphs with Query Sequence." Data Science and Engineering 6, no. 3 (May 29, 2021): 339–57. http://dx.doi.org/10.1007/s41019-021-00158-0.
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AbstractNow, with the prevalence of social media, such as Facebook, Weibo, how to maximize influence of individuals, products, actions in new media is of practical significance. Generally, maximizing influence first needs to identify the most influential individuals since they can spread their influence to most of others in the social media. Many studies on influence maximization aimed to select a subset of nodes in static graphs once. Actually, real graphs are evolving. So, influential individuals are also changing. In these scenarios, people tend to select influential individuals multiple times instead of once. Namely, selections are raised sequentially, forming a sequence (query sequence). It raises several new challenges due to changing influential individuals. In this paper, we explore the problem of Influence Maximization over Streaming Graph (SGIM). Then, we design a compact solution for storing and indexing streaming graphs and influential nodes that eliminates the redundant computation. The solution includes Influence-Increment-Index along with two sketch-centralized indices called Influence-Index and Reverse-Influence-Index. Computing influence set of nodes will incur a large number of redundant computations. So, these indices are designed to keep track of the nodes’ influence in sketches. Finally, with the indexing scheme, we present the algorithm to answer SGIM queries. Extensive experiments on several real-world datasets demonstrate that our method is competitive in terms of both efficiency and effectiveness owing to the design of index.