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This book is a cross-national analysis of the role of the internet in national electoral campaigns. It covers an array of electoral and party systems throughout the globe from parliamentary to presidential, party-based to candidate-oriented, multi-party to two-party, and stable party system to dynamic party system. It takes a look at three groups of nations with varying levels of Internet access—those where internet usage is common across demographic groups, those where usage has reached significant levels but not widespread penetration, and those where internet access is still limited to a small elite. Each chapter is a study of a particular nation, focusing on its electoral and party systems, the accessibility of the Internet to the population, the nature of candidate/party usage, and the effects of the internet on the conduct of campaigns. By reviewing the findings from these studies, Making a Difference draws conclusions about exactly how the internet influences electoral politics.

This title provides a portrait of the role of campaign web sites in American elections. How do candidates use the Internet to gain or reinforce voter support? Are voters influenced by what they see on candidate's web sites? Do they learn anything? The authors answer these questions using data and evidence about the 2000 election.

Large scale analysis and statistics of socio-technical systems that just a few short years ago would have required the use of consistent economic and human resources can nowadays be conveniently performed by mining the enormous amount of digital data produced by human activities. Although a characterization of several aspects of our societies is emerging from the data revolution, a number of questions concerning the reliability and the biases inherent to the big data \"proxies\" of social life are still open. Here, we survey worldwide linguistic indicators and trends through the analysis of a large-scale dataset of microblogging posts. We show that available data allow for the study of language geography at scales ranging from country-level aggregation to specific city neighborhoods. The high resolution and coverage of the data allows us to investigate different indicators such as the linguistic homogeneity of different countries, the touristic seasonal patterns within countries and the geographical distribution of different languages in multilingual regions. This work highlights the potential of geolocalized studies of open data sources to improve current analysis and develop indicators for major social phenomena in specific communities.

Conventional wisdom assumes that increased censorship will strictly decrease access to information. We delineate circumstances when increases in censorship expand access to information for a substantial subset of the population. When governments suddenly impose censorship on previously uncensored information, citizens accustomed to acquiring this information will be incentivized to learn methods of censorship evasion. These evasion tools provide continued access to the newly blocked information—and also extend users’ ability to access information that has long been censored. We illustrate this phenomenon using millions of individual-level actions of social media users in China before and after the block of Instagram. We show that the block inspired millions of Chinese users to acquire virtual private networks, and that these users subsequently joined censored websites like Twitter and Facebook. Despite initially being apolitical, these new users began browsing blocked political pages on Wikipedia, following Chinese political activists on Twitter, and discussing highly politicized topics such as opposition protests in Hong Kong.

Ngo Van Long’s classic paper on the risk of expropriation of natural resources published in a 1975 issue of the Journal of Economic Theory was an instant classic, which spawned a huge literature. Here I pay tribute to this wonderful brilliant yet modest scholar by briefly reviewing his contribution and then sketching how his insights can be used to analyse dynamic conflict over natural resources both as expropriation game and as a differential game on which Long has published extensively too. We discuss three results. First, if an incumbent faces a threat of a rival faction, extraction is more voracious if the factions do not share rents equally. Second, never-ending political conflict cycles are more inefficient if constitutional cohesiveness or rent sharing is strong and political instability is high. Third, resource wars are more intense if rent sharing is weak, reserves of resources are high, the wage is low, and elections occur less frequently.

With the development of the Internet, the oversight of research integrity issues has extended beyond the scientific community to encompass the whole of society. If these issues are not addressed promptly, they can significantly impact the research credibility of both institutions and scholars. This article proposes a text convolutional neural network based on SMOTE to identify short texts of potential public opinion events related to suspected scientific integrity issues from common short texts. The SMOTE comprehensive sampling technique is employed to handle imbalanced datasets. To mitigate the impact of short text length on text representation quality, the Doc2vec embedding model is utilized to represent short text, yielding a one-dimensional dense vector. Additionally, the dimensions of the input layer and convolution kernel of TextCNN are adjusted. Subsequently, a short text event extraction model based on TF-IDF and TextRank is proposed to extract crucial information, for instance, names and research-related institutions, from events and facilitate the identification of potential public opinion events related to suspected scientific integrity issues. Results of experiments have demonstrated that utilizing SMOTE to balance the dataset is able to improve the classification results of TextCNN classifiers. Compared to traditional classifiers, TextCNN exhibits greater robustness in addressing the problems of imbalanced datasets. However, challenges such as low information content, non-standard writing, and polysemy in short texts may impact the accuracy of event extraction. The framework can be further optimized to address these issues in the future.

Clustering methods are promising tools for ensuring the network scalability and maintainability of large-scale flying ad hoc networks (FANETs). However, due to the high mobility and limited energy resources of unmanned aerial vehicles (UAVs), it is difficult to maintain the network reliability and extend the network life of FANETs. In this paper, a new K-means algorithm is developed, and a dynamic transmission power of the cluster heads based clustering (DTPCH-C) scheme is proposed. The goal of this scheme is presented for FANETs to improve the reliability and lifetime of FANETs. Firstly, the optimal number of clusters is calculated and the initial UAV clusters are set up by a K-means algorithm. Then, using a weighted clustering algorithm, the adaptive node degree, the node energy and the distance from the cluster head are weighted and summed for the cluster head election. In the process of inter-cluster communication, the cluster head adjusts its transmit power in real-time through meshing and mobile prediction, thus saving the energy consumption and improving the network lifetime. The proposed DTPCH-C simultaneously optimizes the cluster number, the cluster head energy consumption, the selected cluster head, and the cluster maintenance process. The simulation results show that compared with traditional clustering methods, the proposed DTPCH-C has obvious advantages in terms of the network reliability, network life, and energy consumption.

Distributed flying ad hoc networks (FANETs) have been widely used in collaborative reconnaissance, situation construction, and other scenarios. In distributed FANETs with multi-hop and intermittent links, nodes only maintain neighbors’ information and cannot obtain the whole network messages. There may be contradicting information collected across nodes, resulting in inconsistency problems. However, existing research on collaborative consensus focuses mainly on the control domain using multi-agent consensus theory. The study on distributed network consensus does not consider the effect of the multi-hop forwarding order, hence limiting the optimization of distributed FANETs. Based on this, we establish a network consensus model utilizing the multi-agent consensus theory and analyze the impact of the outage probability of links and untimely forwarding on the distributed consensus probability, considering the node density, link outage probability, and network maintenance times. Besides, using the election mechanism as an example, we establish distributed network performance analysis models considering consensus error to enhance the service delay and resource efficiency performance analysis of distributed FANETs. Finally, we construct a protocol-level simulation platform based on Visual Studio and extensive experiments to determine the optimal mechanism parameters under different network and channel parameters. The simulation results show that the optimal network maintenance times increase with the increasing outage probability of links. Moreover, distributed FANETs can achieve optimal resource efficiency without achieving complete consensus, that is, there is a tradeoff between network maintenance cost and network performance.

Although many developments surrounding the Internet campaign are now considered to be standard fare, there were a number of new developments in 2016.Drawing on original research conducted by leading experts, The Internet and the 2016 Presidential Campaign attempts to cover these developments in a comprehensive fashion.

Traditional voting and bidding systems largely rely on paperwork and human resources throughout the voting process, which can incur high costs in terms of both time and money. Electronic voting and electronic bidding systems can be used to reduce costs, and many new systems have been introduced. However, most systems require a powerful and trusted third party to guarantee system integrity and security. With developments in blockchain technology, research has begun to highlight the core concept of decentralization. In this study, we introduce the first decentralized electronic voting and bidding systems based on a blockchain and smart contract. We also use cryptographic techniques such as oblivious transfer and homomorphic encryptions to improve privacy protection. Our proposed systems allow voters and bidders to participate in the opening phase and improve participant anonymity, the privacy of data transmission, and data reliability and verifiability. Moreover, compared with other electronic voting and bidding systems, our systems are safer and more efficient.