Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd common knowledge how companies thrive by sharing what they know that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.

Common Knowledge: How Companies Thrive by Sharing What They Know

Social media has become a useful tool for learning and teaching due to its functions for knowledge sharing, such as documents exchange, virtual communication, and knowledge formation. Higher education institutions (HEIs) that recognise the value of social media and the importance of individual motivation have sought to encourage its use to bolster learning performance. Using social cognitive theory and connectivism theory, we developed a model that theorises the influence of social media and individual motivation (reputation and altruism) on knowledge sharing and learning performance among students in higher education. To test the model, we employed covariance-based structural equation modelling (CB SEM) on survey data that was collected using questionnaires from 407 students enrolled in the top 10 private universities in West Malaysia. Congruently, we established the utility of the Heterotrait-Monotrait (HTMT) ratio (which was traditionally confined to variance-based or partial least squares SEM) as a discriminant validity measure for CB SEM. As a result, our study empirically evidences that social media functions (documents exchange, virtual communication, and knowledge formation) and individual motivation (reputation) are core factors that HEIs can leverage to encourage knowledge sharing and improve learning performance among tertiary students. The paper concludes with a discussion of its implications, limitations, and future research directions.

Individual motivation and social media influence on student knowledge sharing and learning performance: Evidence from an emerging economy

As the number and the diversity of news outlets on the Web grow, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole. 
In this paper, we begin to fill this gap by studying mainstream and alternative news shared on Twitter, Reddit, and 4chan. By analyzing millions of posts around several axes, we measure how mainstream and alternative news flows between these platforms. Our results indicate that alt-right communities within 4chan and Reddit can have a surprising level of influence on Twitter, providing evidence that "fringe" communities often succeed in spreading alternative news to mainstream social networks and the greater Web.

The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and Alternative News Sources

Instant messaging platforms such as Telegram and WhatsApp became one of the main means of communication used by people all over the world. In most of these services, communities are created around the so called groups and channels, allowing easy, encrypted and instantaneous information exchange. With the political debate gaining a widespread attention from the public and permeated with intense discussion and polarization, specially in a context in which far right communities are being banned from maistream social networks like Twitter, Youtube, and Facebook, alternative platforms, like Telegram become very popular as they start to be seeked as a "free space to discussion" and abused for dissemination of misinformation and hate speech. This work consists in a data analysis for Brazilian public groups and channels for political discussion on Telegram, observing the network created in the platform as well as a closer look in the dynamics of messages and members in this platform. Our findings show that political mobilization increased substantially on Telegram in recent years, suggesting a mass migration from other mainstream platforms. We find the large groups structure of Telegram are effective in spreading the messages through the network, with the content being viewed by numerous users and forwarded multiple times. Looking at the messages, we find an expressive interplay between Telegram and external web pages, notably for Youtube and other social networks. Furthermore, we observed a relevant amount of messages attacking political personalities and spreading unchecked content about COVID-19 pandemic. Taken all together, we perform an extense study in how political discussion advanced on Telegram in Brazil and how they operate in this alternative messaging application.

Towards Understanding the Use of Telegram by Political Groups in Brazil

Crowdsourced Behavior-Driven Development

A great number of people use mobile social networks (MSNs) to communicate, entertain, learn, search and get advice. Growth and survival of any community depends on the activities of its members in sharing information and knowledge. The purpose of this study is to assess the influential factors on knowledge sharing behavior in MSNs in different perspectives in a comprehensive manner.,A model of factors affecting knowledge sharing behavior in MSNs is proposed by applying the structural equation modeling and path analysis to data collected from a sample of users of a well-known MSN through a questionnaire.,This study supports the contributive aspects of trust and enjoying participation in sharing knowledge, while there is no significant correlation between perceived ease of use and knowledge sharing behavior in MSNs. Furthermore, intention to share knowledge can lead to actual behavior in MSNs environments.,The results obtained here provide a grasp of factors that influence knowledge sharing in mobile communities which would promote enhanced contribution towards their online communities by MSNs administrators.,A four-dimensional comprehensive model consisting of social, psychological, cultural and technological perspectives in one package is proposed here for knowledge sharing behavior in MSNs. Such a comprehensive perspective is overlooked in the existing literature.

A multidimensional model of knowledge sharing behavior in mobile social networks

Online messaging platforms such as WhatsApp, Telegram, and Discord, each with hundreds of millions of users, are one of the dominant modes of communicating or interacting with one another. Despite the widespread use of public group chats, there exists no systematic or detailed characterization of these group chats. There is, more importantly, lack of a general understanding of how these (public) groups differ in characteristics and use across the different platforms. We also do not know whether the messaging platforms expose personally identifiable information, and we lack a comprehensive view of the privacy implications of leaks for the users.In this work, we address these gaps by analyzing the messaging platforms' ecosystem through the lens of a popular social media platform---Twitter. We search for WhatsApp, Telegram, and Discord group URLs posted on Twitter over a period of 38 days and amass a set of 351K unique group URLs. We analyze the content accompanied by group URLs on Twitter, finding interesting differences related to the topics of the groups across the multiple messaging platforms. By monitoring the characteristics of these groups, every day for more than a month, and, furthermore, by joining a subset of 616 groups across the different messaging platforms, we share key insights into the discovery of these groups via Twitter and reveal how these groups change over time. Finally, we analyze whether messaging platforms expose personally identifiable information. In this paper, we show that (a) Twitter is a rich source for discovering public groups in the different messaging platforms, (b) group URLs from messaging platforms are ephemeral, and (c) the considered messaging platforms expose personally identifiable information, with such leaks being more prevalent on WhatsApp than on Telegram and Discord.

Demystifying the Messaging Platforms' Ecosystem Through the Lens of Twitter

Wireless sensor networks (WSNs) are mostly implemented in outdoor and harsh environments; consequently, the probability of node failure is high and applying the fault tolerance mechanisms becomes crucial. The relay nodes placement is a mechanism in order to increase the connectivity level of WSNs. In this paper, a new method (KCN) is proposed to create k-connected WSNs based on injection of relay nodes in heterogeneous WSNs, where sensor nodes have different transmission radii. KCN consists of three algorithms. Algorithm 1 creates a minimum k-vertex connected graph based on graph theory and Algorithm 2 determines the candidate relay nodes and their locations for implementing each edge in the obtained graph. Algorithm 3 minimizes the deployed relay node count based on two new proposed lemmas. The main issues of concern here are the time complexity and the deployed relay node count. KCN has a low time complexity of O(n2), where n is the sensor node count. The simulation results demonstrate that KCN applies less relay nodes than the most closely related approach.

k-Connected Relay Node Deployment in Heterogeneous Wireless Sensor Networks

QAnon is a far-right conspiracy theory that became popular and mainstream over the past few years Worryingly, the QAnon conspiracy theory has implications in the real world, with supporters of the theory participating in real-world violent acts like the US capitol attack in 2021 At the same time, the QAnon theory started evolving into a global phenomenon by attracting followers across the globe and, in particular, in Europe Therefore, it is imperative to understand how the QAnon theory became a worldwide phenomenon and how this dissemination has been happening in the online space This paper performs a large-scale data analysis of QAnon through Telegram by collecting 45M messages posted in 161 QAnon groups/channels Using Google's Perspective API, we analyze the toxicity of QAnon content across languages and over time Also, using a BERT-based topic modeling approach, we analyze the QAnon discourse across multiple languages Among other things, we find that the German language is prevalent in QAnon groups/channels on Telegram, even overshadowing English after 2020 Also, we find that content posted in German and Portuguese tends to be more toxic compared to English Our topic modeling indicates that QAnon supporters discuss various topics of interest within far-right movements, including world politics, conspiracy theories, COVID-19, and the anti-vaccination movement Taken all together, we perform the first multilingual study on QAnon through Telegram and paint a nuanced overview of the globalization of the QAnon theory

Mohamad Hoseini

Papers

A multidimensional model of knowledge sharing behavior in mobile social networks

Demystifying the Messaging Platforms' Ecosystem Through the Lens of Twitter

k-Connected Relay Node Deployment in Heterogeneous Wireless Sensor Networks

On the Globalization of the QAnon Conspiracy Theory Through Telegram