The recent rapid development of Web technology, multimedia content, and interactive data has considerably expanded the size of the Internet transmissions. Benefiting from the paradigm-shifting technology of software defined networking (SDN), the administrators are now able to easily manage network flows by customizing flow rules over SDN. Inspired by this, we propose a UDP-based reliable transmission framework to improve efficiency of transmission control protocol (TCP) transmission on an SDN-enabled network. The main idea of our framework is to convert the TCP transmission into UDP packets to decrease the overhead during communications, such as handshaking, acknowledgment, and header overhead while using TCP. To guarantee reliability, we have leveraged the power of SDN to designate packets under our protocol to flow in predefined routes and monitor them to avoid possible packet loss. Our proposal is composed of a series of designs and implementations, including the packet format transformations, packet buffering, and retransmission mechanisms on switches. For users, this means that they are transmitting data with TCP, while the overhead of the TCP traffic is reduced significantly through a reliable and lightweight UDP transmission mechanism on the SDN-enabled network. Our evaluation results show that our framework provides a more efficient bandwidth usage and guarantees the reliability of packets as in TCP transmissions.

Due to the increasing prominence of social network services, political communication has experienced a paradigm shift. To communicate with internet users, politicians, candidates, and political organizations create fan pages. Initially, they provide text-only material on their pages; later, to increase engagement, they include photos, images, and videos. This paper investigates image-based political images in Taiwan for the first time during a nationwide referendum. Unlike an election, a referendum is a policy-based vote. We evaluate over 2000 Facebook images shared by the two most prominent political parties in order to comprehend the aspects of images and the tactics of political organizations. In addition, we examine the textual content, objects, and colors of the collected data. We find that the characteristics of propaganda materials vary between political groups. Nonetheless, the color strategies employed by both sides are equivalent, with each side utilizing its own representative colors for consolidation and the opponent’s colors for attacking.

In recent years, online social networks have become vital platforms for political discussions as well as electoral campaigns. Political organizations have leveraged social media as a tool for distributing information, monitoring public opinion, promoting candidates, and even influencing the election outcomes. To achieve their goals, groups or organizations may purchase or register plenty of accounts, the so-called sockpuppets, to shape the opinion and influence the public atmosphere. In this article, we investigate sockpuppet accounts based on their activity behaviors. A series of user behavior based, network-based, content-based features, and temporal features are proposed and extracted for the classification task. To categorize users into sockpuppets and legitimate users, we employ ensemble learning as well as single classifiers and a thorough evaluation is presented. Experimental results on an officially verified dataset obtained from the largest social forum in Taiwan show that a maximum value of area under the curve score achieves 0.904 via an ensemble learning with feature engineering in our proposal.

The evolution of political campaigns is evident with the ascent of social media. Ideological beliefs are increasingly disseminated through political-affiliated fan pages. The interaction between politicians and the general public on these platforms plays a pivotal role in election outcomes. In this study, we utilize a multimodal approach to explore and quantify similarities of ideologies among political fan pages. we employed visualization techniques to demonstrate the political stance of each fan page. To validate our proposal, we concentrated on an analysis of the 2021 national referendums in Taiwan, encompassing a collection of fan pages and their corresponding posts that were related to these referendums. Through a qualitative analysis of the content of these fan pages, the efficacy of our multimodal framework in clustering fan pages according to their respective political ideologies was evaluated. The findings of this study underscore the significant enhancement in the accuracy of stance detection when integrating multiple modalities of data, namely textual content, visual imagery, and user interactions.

Online social media platforms play an important role in political communication where users can freely express and exchange their political opinion. Political entities have leveraged social media platforms as essential channels to disseminate information, interact with voters, and even influence public opinion. For this purpose, some organizations may create one or more accounts to join online political discussions. Using these accounts, they could promote candidates and attack competitors. To avoid such misleading speeches and improve the transparency of the online society, spotting such malicious accounts and understanding their behaviors are crucial issues. In this paper, we aim to use network-based analysis to sense influential human-operated malicious accounts who attempt to manipulate public opinion on political discussion forums. To this end, we collected the election-related articles and malicious accounts from the prominent Taiwan discussion forum spanning from 25 May 2018 to 11 January 2020 (the election day). We modeled the discussion network as a multilayer network and used various centrality measures to sense influential malicious accounts not only in a single-layer but also across different layers of the network. Moreover, community analysis was performed to discover prominent communities and their characteristics for each layer of the network. The results demonstrate that our proposed method can successfully identify several influential malicious accounts and prominent communities with apparent behavior differences from others.

Background: In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020. Objective: The aim of this study was to examine the propagation patterns and textual transformation of COVID-19–related rumors on a closed social media platform. Methods: We obtained a data set of suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a “rumor group.” Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor. Results: A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of the 396 COVID-19–related rumors, 134 (33.8%) had been fact-checked by the International Fact-Checking Network–certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19–related messages, compared to non–COVID-19–related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations. Conclusions: To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas.