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Online Social Media (OSM) are dominating the wide range of Internet services. Due to their vast audience, it is crucial to evaluate the interpersonal trust among OSM users that can identify reliable sources of information, the meaningfulness of a relationship, or the trustworthiness of other users. SentiTrust is an innovative trust model for Decentralized Online Social Networks that is based on AI-powered Sentiment Analysis. It enriches the trust definition by exploiting important features that are enabled because of the adoption of Social Media through mobile devices. The model can be easily extended and customized according to the scenario of interest. The sentiment analysis component has been tested by involving 30 participants who completed several guided tasks using a social media application while their electrodermal activity and rate responses were measured. The results suggest that low arousal states are related to receiving happy faces and to sending more messages per minute. Furthermore, positive interactions result in shorter interactions and multimedia exchanges.

Non-fungible tokens (NFT) represent one of the most important technologies in the space of Web3. Thanks to NFTs, digital or physical assets can be tokenised to represent their ownership through the usage of smart contracts and blockchains. The first generation of this technology, called NFT 1.0, considers static tokens described by a set of metadata that cannot be changed after token creation. The static nature prevents their wide spread as they do not support any meaningful user interaction. For this reason, its evolution, called NFT 2.0, has been proposed to make tokens interactive and dynamic and enhance user experience, opening the possibility to use NFTs in more ways and scenarios. The purpose of this article is to review the transition from NFT 1.0 to NFT 2.0, focusing on the newly introduced properties and features and the rising challenges. In particular, we discuss the technical aspects of blockchain technology and its impact on NFTs. We provide a detailed description of NFT properties and standards on various blockchains and discuss the support of the most important blockchains for NFTs. Then, we discuss the properties and features introduced by NFT 2.0 and detail the technical challenges related to metadata and dynamism. Lastly, we conclude by highlighting the new application scenarios opened by NFT 2.0. This review paper serves as a solid base for future research on the topic as it highlights the current technological challenges that must be addressed to help a wide adoption of NFTs 2.0.

Since their introduction, Online Social Networks (OSNs) have transformed the way people interact with each other. Lately, a new trend is rising in the development of OSNs, fueled by an increasing interest of the blockchain technology and the benefits it can bring to the world of OSNs. Blockchain Online Social Media (BOSMs) are Social Media applications that are supported by the blockchain technology. Thanks to a blockchain, BOSMs either try to enforce the privacy of the users or try to redistribute with their users the economic wealth generated by the platform through a rewarding system. There are countless BOSMs available which incorporate a rewarding system. Among them, Steemit can be considered the most well-known platform exceeding 1 million registered users. Steemit is supported by the blockchain Steem, which is a blockchain that natively supports the development of social applications by the usage of transactions that model social activity. Even if other important blockchains, such as Ethereum has been widely analysed, at the best of our knowledge, no study exists concerning the topology of the transactions graph of Steem. The main goal of this paper is to study the structure of the Steem transaction graph to understand its characteristics and unveil crucial knowledge concerning their users. More in detail, we build the Interactions Graph and, after its study, we evaluate three subgraphs that capture its social and monetary aspects. The degree distributions of the graphs follow a power-law. Additionally, we detect a substantial number of bots that offer paid services on the platform among the most active users. Lastly, the investigation of the four analysed graphs through a bow-tie structure, suggesting that half of the users have a passive social behaviour and that 80% of the users tend to accrue economic value

Interest in social media has only increased with time [...]

Social media is becoming one of the dominant ways to communicate. Before social media, people were extremely limited in their means to interact with others, and they were limited largely to the people that they knew in person. However, this impact on people in real life has damaged privacy. Alternative solutions have been proposed in order to overcome current social media issues. In this direction, blockchain is one of the most promising, and several blockchain-based social media have been proposed. In this paper, we analyze blockchain online social media from the technical point of view in order to understand the current trend of social DApps and to describe which characteristics are important in a blockchain-based social media scenario. We analyze real data by exploiting one of the most well-known DApps sites, and we compare current technologies in order to highlight which ones can be better applied to a real social scenario, such as Facebook.

One of the main ideas about the Internet is to rethink its services in a user-centric fashion. This fact translates to having human-scale services with devices that will become smarter and make decisions in place of their respective owners. Online Social Networks and, in particular, Online Social Groups, such as Facebook Groups, will be at the epicentre of this revolution because of their great relevance in the current society. Despite the vast number of studies on human behaviour in Online Social Media, the characteristics of Online Social Groups are still unknown. In this paper, we propose a dynamic community detection driven study of the structure of users inside Facebook Groups. The communities are extracted considering the interactions among the members of a group and it aims at searching dense communication groups of users, and the evolution of the communication groups over time, in order to discover social properties of Online Social Groups. The analysis is carried out considering the activity of 17 Facebook Groups, using 8 community detection algorithms and considering 2 possible interaction lifespans. Results show that interaction communities in OSGs are very fragmented but community detection tools are capable of uncovering relevant structures. The study of the community quality gives important insights about the community structure and increasing the interaction lifespan does not necessarily result in more clusterized or bigger communities.