News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19’s communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This article presents a large-scale study based on data mined from Twitter . Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users’ profiles, meta-data and tweets’ context. The study noted various interesting conclusions including the critical impact in term of reach level of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.

/pdf/critical-impact-of-social-networks-infodemic-on-defeating-54nzpkpwd4.pdf

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

New trends in influence maximization models

There is a need to understand how to: spread product information to maximum range, identifying influential users, and analyze how they are intrinsically connected in a social network. In this paper, we collected tweets of Huawei Mate 9 to analyze users’ information behavior such as tweeting, forwarding, and commenting on tweets. We applied independent cascade model to this empirical Twitter diffusion network, and found it is proper to fit to the product information diffusion process. Using its network structure and PageRank measurement, we can identify influential nodes, and interpret the intrinsic connection between these influential nodes. Further, it is significant to consider the node’s background, such as interest, occupation, and country when identifying influential nodes. And it is discussed that the tweet content related to novel technology may attract more participation in ordinary users.

Product information diffusion in a social network

The rapid growth of the World Wide Web and social media allows users playing an active role in the contents’ creation process. Users can evaluate the brands’ reputation and quality exploiting the information provided by new marketing channels, such as social media, social networks , and electronic commerce (or e-commerce). Consequently, enterprises need to spot and analyze these digital data in order to improve their reputation among the consumers. The aim of this chapter is to highlight the common approaches of sentiment analysis in social media streams and the related issues with the cloud computing , providing the readers with a deep understanding of the state of the art solutions.

Big data sentiment analysis for brand monitoring in social media streams by cloud computing

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19 communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This paper presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users profiles, meta-data and tweets context. The study noted various interesting conclusions including the critical impact of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.

/pdf/critical-impact-of-social-networks-infodemic-on-defeating-1ee9ctbdud.pdf

When Influence Maximization (IM) is applied to social network to maximise the network coverage, it becomes an effective mechanism for marketing applications. In this paper, we focus on a specific influence maximization problem, i.e., selecting a set of seeds on twitter to maximise information propagation, which can be used for information reaching out in marketing campaigns. The proposed approach is taking into the consideration of social ties, user interactions, and information propagation on Twitter. The influence probability is calculated according to users' action history including tweet, favourite, mention/reply, and retweet. An information diffusion model is proposed with the capability to simulate the dynamic process of information spread on Twitter. A concise heuristic algorithm is developed for influence maximization accordingly. Experimental results and analysis are provided based on a real Twitter network including 3,292 users in Darwin city in Australia.

Influence maximization on twitter: A mechanism for effective marketing campaign

Influence Maximization (IM) has received lots of attention from academia as well as industry. In this work, we study a specific influence maximization problem, selecting a set of seed users to maximize the effectiveness of advertising campaigns on Twitter. This problem requires that the information diffusion model must have the capability to support: (a) an active user can make multiple attempts to activate his neighbors, and (b) a user can accept an advertising message many times. Considering the characteristics of advertisement propagation on Twitter, we propose a diffusion model that inherits the classic independent cascade model and removes the constraints such as single attempt in activating neighbors and taking single influence from other users. The influence probability is calculated based on users' action history including tweet, favorite, reply and retweet. Influence decay is also introduced to reflect the temporal features associated with influence. A new metric "advertising effectiveness" is defined as the maximization objective. Several existing seed selection algorithms are analyzed based on the proposed diffusion model against a real dataset from Twitter including 3,292 users in Darwin city in Australia. Experimental results and analysis are provided to show the soundness of the proposed model.

Maximizing the Effectiveness of Advertising Campaigns on Twitter

Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/government communication. These activities have used mostly Twitter content to derive topics. More recently, tweet interactions have also been considered, leading to better topics. Given the dynamic aspect of Twitter, we hypothesize that temporal features could further improve topic derivation on a Twitter collection. In this paper, we first perform experiments to characterize the temporal features of the interactions in Twitter. We then propose a time-sensitive topic derivation method. The proposed method incorporates temporal features when it clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.

Time-Sensitive Topic Derivation in Twitter

The Internet of Things (IoT) has become increasingly popular, and its communication requirements have grown beyond what traditional ground networks can handle. The space–air–ground (SAG) integrated network has been proposed as a potential solution, where unmanned aerial vehicles (UAVs) collect data from IoT devices and transmit them to satellites. However, the limited energy of UAVs is one of the key factors restricting communication performance, so it is necessary to consider communication energy efficiency (EE). In addition, the improvement of EE will bring about the decline of spectral efficiency (SE). Therefore we consider the tradeoff between EE and SE. Considering a SAG-IoT network, the focus of the paper is to optimize sub-channel selection, power control, and UAV position deployment to maximize EE and SE of the network, which is a multi-objective optimization (MOO) problem. On this premise, we try to improve the data throughput between IoT devices and UAVs. To solve this MOO problem, we use the ϵ-constraint to convert it into a single-objective optimization problem. We then employ various optimization algorithms such as the Dinkelbach algorithm, successive convex approximation algorithm, Lagrange dual algorithm, and block coordinate descent algorithm to solve the mixed integer non-convex problem. Simulation results show that the proposed algorithm converges to at least one sub-optimal solution.

Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks

This paper proposes a hybrid feature selection method for predicting user influence on Twitter. A set of candidate features from Twitter is identified based on the five attributes of influencers defined in sociology. Firstly, less relevant features are filtered out with a feature-weighting algorithm. Then the Sequential Backward Floating Selection is utilized as the search strategy. A Back Propagation Neural Network is employed to evaluate the feature subset at each step of searching. Finally, an optimal feature set is obtained for predicting user influence with a high degree of accuracy. Experimental results are provided based on a real world Twitter dataset including seven million tweets associated with 200 popular users. The proposed method can provide a set of features that could be used as a solid foundation for studying complicated user influence evaluation and prediction.

A Hybrid Feature Selection Method for Predicting User Influence on Twitter

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