Campaign News Research Articles

A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling.

Background/Objectives: This study aimed to identify the main issues related to public patient safety campaigns reflected in Korean online news. This study utilized a text-mining method to identify keywords and topics related to patient safety campaigns. Methods: The data collection period was from 1 January 2022 to 31 December 2023, and 4110 news articles were extracted. Through data preprocessing, 2661 duplicated news and 1213 unrelated news were removed, and 236 news were selected. Using the NetMiner program, keyword co-occurrence frequency calculation, keyword centrality analysis, and topic modeling analysis were performed. Results: The results showed that the most frequently mentioned keywords with high degree centrality, betweenness centrality, and closeness centrality in online news were "hospital", "medical", "medicine", "project", and "treatment". The topics of online news related to the patient safety campaign were "patient-centered care for medical safety", "health promotion projects at a regional institution", "hand hygiene education to prevent infection", "healthcare quality improvement through the Mint Festival", and "safe use of medicines". Conclusions: This study analyzed patient safety campaign news topics using text network analysis and topic modeling. It was confirmed that patient safety campaigns are essential for fostering a patient safety culture, improving medical quality, and encouraging patient participation in hospitals. Therefore, to build a safe medical environment, it is necessary to establish an effective patient safety campaign for not only medical staff providing medical care, but also patients and their caregivers, and for this, cooperation and participation from various professional occupations are necessary.

Fake social media news and distorted campaign detection framework using sentiment analysis & machine learning

Social networking platforms have become one of the most engaging portals on the Internet, enabling global users to express views, share news and campaigns, or simply exchange information. Yet there is an increasing number of fake and spam profiles spreading and disseminating fake information. There have been several conscious attempts to determine and distinguish genuine news from fake campaigns, which spread malicious disinformation among social network users. Manual verification of the huge volume of posts and news disseminated via social media is not feasible and humanly impossible. To overcome the issue, this research presents a framework to use sentiment analysis based on emotions to investigate news, posts, and opinions on social media. The proposed model computes the sentiment score of content-based entities to detect fake or spam and detect Bot accounts. The authors also present an investigation of fake news campaigns and their impact using a machine learning algorithm with highly accurate results as compared to other similar methods. The results presented an accuracy of 99.68 %, which is significantly higher as compared to other methodologies delivering lower accuracy.

Detection and Analysis of Fake News Users’ Communities in Social Media

The widespread use of social media platforms has led to an increase in the dissemination of fake news with the intention of manipulating public opinion and causing chaos and panic among the population. To address this issue, we focus on detecting the organized groups that participate together in fake news campaigns without prior knowledge of the news content or the profiles of social accounts. To this end, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial–temporal similarity graph</i> , a novel graph structure that connects social accounts that participate in the early stage of similar fake news campaigns. A community detection algorithm is applied on the similarity graph to cluster the users into communities. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">community labeling algorithm</i> to label the communities as benign or malicious based on the output of a fake news classifier. Evaluation results show that the community labeling algorithm can correctly label the communities with an accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99.61\%$</tex-math> </inline-formula> . In addition, we perform a statistical comparison analysis to identify the structural community features that are statistically significant between benign and malicious communities.

Artificial intelligence—friend or foe in fake news campaigns

In this paper the impact of large language models (LLM) on the fake news phenomenon is analysed. On the one hand decent text‐generation capabilities can be misused for mass fake news production. On the other, LLMs trained on huge volumes of text have already accumulated information on many facts thus one may assume they could be used for fact‐checking. Experiments were designed and conducted to verify how much LLM responses are aligned with actual fact‐checking verdicts. The research methodology consists of an experimental dataset preparation and a protocol for interacting with ChatGPT, currently the most sophisticated LLM. A research corpus was explicitly composed for the purpose of this work consisting of several thousand claims randomly selected from claim reviews published by fact‐checkers. Findings include: it is difficult to align the responses of ChatGPT with explanations provided by fact‐checkers; prompts have significant impact on the bias of responses. ChatGPT at the current state can be used as a support in fact‐checking but cannot verify claims directly.

Newspapers and Primacies in Elections: A Comparative Study of Coverage in Two Mainstream English Dailies

Elections are the mechanism under a democratic system to form a government. Invariably, during elections, the power remains in the hands of the people- being able to demonstrate their grumbles and elect the representatives of their choice. To choose among alternatives, people need to know the candidates and political parties with reference to various public issues. The mass media are the channels through which public could able to know socio-political, and economic issues in their country. In the modern marketing driven media world, news about public interests has diminished due to over-dependence of news media on sensational political stuff. Media has obligation to give adequate coverage to election campaign news. It is largely through media that people are able to know their leaders and manifestoes of different political parties. This paper aimed at analyzing newspaper coverage relevant to election campaigns. For the purpose, two mainstream English Dailies -The Times of India and The Hindu have been chosen for analysis. The Study explores the press coverage devoted to political parties, candidates contesting the elections and major public issues like corruption, crime, economy, defence or foreign policy etc. during the elections. Moreover, this research shows how news media play a significant role in shaping discourse on public issues.

The Polarizing Impact of Political Disinformation and Hate Speech: A Cross-country Configural Narrative.

Information and communication technologies hold immense potential to enhance our lives and societal well-being. However, digital spaces have also emerged as a fertile ground for fake news campaigns and hate speech, aggravating polarization and posing a threat to societal harmony. Despite the fact that this dark side is acknowledged in the literature, the complexity of polarization as a phenomenon coupled with the socio-technical nature of fake news necessitates a novel approach to unravel its intricacies. In light of this sophistication, the current study employs complexity theory and a configurational approach to investigate the impact of diverse disinformation campaigns and hate speech in polarizing societies across 177 countries through a cross-country investigation. The results demonstrate the definitive role of disinformation and hate speech in polarizing societies. The findings also offer a balanced perspective on internet censorship and social media monitoring as necessary evils to combat the disinformation menace and control polarization, but suggest that such efforts may lend support to a milieu of hate speech that fuels polarization. Implications for theory and practice are discussed.

#MahsaAmini: Iranian Twitter Activism in Times of Computational Propaganda

ABSTRACT This paper discusses 1) how #MahsaAmini has become a significant hybrid movement in Iran and 2) how it is taking Twitter activism into the post-fake news era. I will begin by reviewing the existing literature on Twitter and democracy. Then, I will discuss the roots of the #MahsaAmini, which make it a hybrid movement. Further, I will show how Iranians have circumvented the regime’s fake news campaigns and cyber army. This paper argues that while non-democratic regimes have developed more complex methods of suppressing digital protests, these efforts are ineffective in large-scale protests.

Framing analysis on Luwuk Post on the news of regional head election campaign (Pilkada) Banggai Regency in 2020

This research aims to find out the framing and construction of Luwuk Post media reality to the news of the 2020 Banggai Regency regional head election. To collect data is done by identifying the overall news document published in the Daily Luwuk Post newspaper during the campaign period based on PKPU No. 13 of 2020 which began from November 22, 2020 to December 5, 2020. The research method used in this research is to use a qualitative descriptive approach. The data sources used in this study are primary data and secondary data. In this study, the data analysis technique used is an analysis tool using Nvivo, Nvivo as an analysis tool that displays data in the form of text and images through the process of coding data. The sample in this study is textually the title of the news of campaign activities in Banggai Regency in the 2020 Election in The Luwuk Post Newspaper Daily period of November 22, 2020 to December 5, 2020 into a sample of research. With the results of the study that the framing of Luwuk Post media on the news of the 2020 Banggai District Regional Election Campaign with the highest percentage consisted of Define problems with a news point of view of 66%, Diagnose Causes with 75% news issues, make moral judgment with a positive frame of 76%, and the last Treatment recommendation with 88% source suggestions. Data coding results through NVIVO 12 Plus show the candidate pair H. Herwin Yatim – H. Mustar Labolo (WINSTAR) packages his interests by making the agenda of government or government policy as the object of news and efforts to influence the lives of readers in the sense of making choices to WINSTAR candidate pairs.

The Campaign Disinformation Divide: Believing and Sharing News in the 2019 UK General Election

ABSTRACT We explain why citizens believed and shared false political information during the 2019 UK general election campaign. In two surveys of samples mirroring the adult population conducted before the vote (total N = 4018), we showed respondents 24 different news statements and asked if they had seen them before, whether they believed them, and how likely they would be to share them on social media. The statements included actual disinformation that was circulating and had been debunked, placebos that did not feature in the campaign but were carefully constructed to resemble actual false statements spreading at the time, and true statements that were being reported during the campaign. We find that the more that respondents received their campaign news via professional news organizations, the more they were able to distinguish true from false information. Conversely, the more that respondents used social media for campaign news, the less they were able to distinguish true from false information. These differences also shaped the intention to share true versus false news, and false news, once perceived as accurate, was more likely to be shared than true news overall. This “campaign disinformation divide” between getting news from professional journalistic organizations and news from social media has important implications for contemporary election campaigns in democratic public spheres.

Detection of fake news campaigns using graph convolutional networks

The detection of organised disinformation campaigns that spread fake news, by first camouflaging them as real ones is crucial in the battle against misinformation and disinformation in social media. This article presents a method for classifying the diffusion graphs of news formed in social media, by taking into account the profiles of the users that participate in the graph, the profiles of their social relations and the way the news spread, ignoring the actual text content of the news or the messages that spread it. This increases the robustness of the method and widens its applicability in different contexts. The results of this study show that the proposed method outperforms methods that rely on textual information only and provide a model that can be employed for detecting similar disinformation campaigns on different context in the same social medium.

Hybrid Deep Learning Model for Fake News Detection in Social Networks (Student Abstract)

The proliferation of fake news has grown into a global concern with adverse socio-political and economical impact. In recent years, machine learning has emerged as a promising approach to the automation of detecting and tracking fake news at scale. Current state of the art in the identification of fake news is generally focused on semantic analysis of the text, resulting in promising performance in automated detection of fake news. However, fake news campaigns are also evolving in response to such new technologies by mimicking semantic features of genuine news, which can significantly affect the performance of fake news classifiers trained on contextually limited features. In this work, we propose a novel hybrid deep learning model for fake news detection that augments the semantic characteristics of the news with features extracted from the structure of the dissemination network. To this end, we first extend the LIAR dataset by integrating sentiment and affective features to the data, and then use a BERT-based model to obtain a representation of the text. Moreover, we propose a novel approach for fake news detection based on Graph Attention Networks to leverage the user-centric features and graph features of news residing social network in addition to the features extracted in the previous steps. Experimental evaluation of our approach shows classification accuracy of 97% on the Politifact dataset. We also examined the generalizability of our proposed model on the BuzzFeed dataset, resulting in an accuracy 89.50%.

Detection of fickle trolls in large-scale online social networks

Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or social bots for propagation. Detection of so-called social trolls in these platforms is challenging due to their large scale and dynamic nature where users’ data are generated and collected at the scale of multi-billion records per hour. In this paper, we focus on fickle trolls, i.e., a special type of trolling activity in which the trolls change their identity frequently to maximize their social relations. This kind of trolling activity may become irritating for the users and also may pose a serious threat to their privacy. To the best of our knowledge, this is the first work that introduces mechanisms to detect these trolls. In particular, we discuss and analyze troll detection mechanisms on different scales. We prove that the order of centralized single-machine detection algorithm is O(n^3) which is slow and impractical for early troll detection in large-scale social platforms comprising of billions of users. We also prove that the streaming approach where data is gradually fed to the system is not practical in many real-world scenarios. In light of such shortcomings, we then propose a massively parallel detection approach. Rigorous evaluations confirm that our proposed method is at least six times faster compared to conventional parallel approaches.

Mass Media and Social Distancing Regulations to Prevent the Spread of the Covid-19 Virus Outbreak in Indonesia

The current outbreak of the corona virus is becoming increasingly worrisome. More and more people became infected with the corona virus, with some even succumbing to their illness and dying as a result of the virus's spread. Today, social media is a popular means of communicating outside traditional means. In order to prevent the spread of this new disease, I'd like to share my experiences as someone who has been infected with the corona virus, as well as to warn others to stay at home and take care of themselves in order to avoid contracting the sickness. Descriptive qualitative research is employed in this study. It is the goal of this study to educate community members and millennial youth with information and an overview so that they can pay more attention to social ties in society and avoid societal changes as a result of false news causing undue worry. According to the study's findings, the community's usage of social media and other online tools has increased its members' access to information. A large false news campaign about social isolation preceded this, despite the government's call to heed the recommendations relating to social isolation.

Perangkat Lunak Kriptografi Metode Gost

Indonesia is holding a democratic party, namely the general elections in 2019. The plurality of parties has represented the many interests of the Indonesian people, who are known to consist of many ethnicities, religions and races. Thus, the interests of the people here are the main thing that must be considered in the 2019 BEM Board Election. The problem that usually arises during campaign momentum is the many hoax (fake) issues circulating that are used to manipulate 2019 campaign news. Spinning this issue is very dangerous, especially for Panca Dharma Vocational School students who are new to political science. Research activities are expected to contribute to opening political insight from an early age among students of Panca Dharma Vocational School

The influence of value co-creation on customer loyalty, behavioural intention, and customer satisfaction in emerging markets

One of the current major trends in the media industry is customer value co-creation. User participation can significantly affect news media organisations. This study examines the impact of value co-creation on the loyalty, satisfaction, and behavioural tendencies of media consumers, using a survey of users of My Fars news platform. The conceptual model of this study is developed and tested by using survey data from 283 users of the news campaign in Fars News and Structural Equation Modelling (SEM) is employed to simultaneously test the hypothesized relationships. The results show that user participation in value co-creation significantly affects their satisfaction, loyalty, and behavioural tendencies.

Political crowdfunding and resource mobilization for collective action: The keys to success

This paper focuses on the determination and analysis of the keys to success in the mobilization of resources through political crowdfunding projects aimed to develop activities linked to collective action for social and political change. For that purpose, based on financial success, we have carried out benchmarking and statistical analysis through diverse variables related to communicative, financial, relational and organizational elements. This study was conducted on 233 campaigns developed in Spain between 2012 and 2017. The results show the importance of the intensity of the campaign posts and the impact through Twitter. Furthermore, as new findings, the partnership between the campaign news in digital media and the financial success should be pointed out as well as the importance of dissemination and the success of cooperation at a communicative level between agents of a socio-political nature. To conclude, we must stress the importance of the organizational and communicative role of the promoters for success and its ability to articulate an external structure configured on identity and technical elements that facilitate the cooperation and activation of ties of different intensity through a media ecosystem, specially Twitter.

First-order linkage analysis (Frequently Applied Designs)

First-order linkage analysis (Frequently Applied Designs)

When Do Environmental Externalities Have Electoral Consequences? Evidence from Fracking

The electoral salience of some issues may diminish when one politician has authority over many policy areas. This study measures the role of environmental regulation in concurrent elections for governors and specialized energy regulators in two U.S. states. I first show that while both offices can influence environmental and energy policies, quantitative analysis of campaign news coverage reveals clear differences in the importance of these issues in the two races. Next, I use geologic variation in earthquakes caused by oil and gas production to measure the electoral consequences of a costly environmental externality. There are measurable effects only in the energy regulator race. These results are consistent with theories of issue bundling. Finally, the unbundling effects that I measure appear to be themselves limited by voter attentiveness and partisanship. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The Uses of Honorifics by President Candidate in The Campaign News Text on National Newspapers

The research aims to describe the types of linguistic tools for honorary excretion by presidential candidates in Indonesian newspaper news texts. The research method uses a qualitative approach. The data is in the form of direct sentence except from news texts that contain honorific expressions. Sources of data in the form of text national news about the presidential election campaign in the media Kompas, Media Indonesia, Republika, Sinar Harapan, and Sound Pembaharuan, published between January-March 2019. The collecting data is done by recording, clicking classify the linguistic tools used by taking fragments of sentence quotes. Data processing is done by identifying, sorting, verifying, interpretation of data, and concluded. The results of the study can be concluded that the types of linguistic tools for honorific expression in the speech of presidential candidates in Indonesian newspaper news texts include 13 types, namely: (a) self-name reference and self-name greeting, (b) self-name reference and self-name greeting as well, (c) greetings of kinship, (d) greetings of personal pronouns, (e) personal pronouns, (f) greetings of social position (social deixis), (g) position references/ranks / professions / social positions, (h) fatis, ( i) passive speech forms in-, (j) expressive modalities, (k) self-defeats, (l) euphemisms, and (m) relational modalities. The limitation of this research is that it still needs to be revealed the factors that influence honorifics, strategies that bring up honorifics, aspects of positive faces and negative faces. The study only focused on the type of use of honorific expressions

Towards Engaged Citizens: Influences of Second Screening on College Students’ Political Knowledge and Participation

ABSTRACT Commonly conducted alongside political TV viewing, second screening is a new media use behavior that merits explorations at diverse levels. But the research on second screening is still in its infancy. To deeply understand the complicated mechanism of second screening and its influence in a political milieu, this study focuses on the second screening activities aroused by televised political campaign news and programs, and (1) identifies specific categories of second screening activities during television campaign exposure, and (2) establishes the indirect influences of each type of second screening activity on individuals’ cognition (political knowledge) and behavior (political participation) through internal (internal political efficacy) and external (political discussion) factors.