Irrelevant Messages Research Articles

Examining the Psychological Mechanisms Underlying Nostalgia Appeals: A Comparative Experiment of Nostalgia and Regret

ABSTRACT Obesity rates remain high among U.S. adults, especially rural residents. Research has shown that nostalgia appeals effectively promote several healthy behaviors. However, the psychological mechanisms underlying nostalgia appeals remain unclear. This study examined the effects of nostalgia appeals on intention to increase exercise and shed light on how nostalgia affected persuasive outcomes. We anticipated that nostalgia appeals would persuade people by enhancing self-esteem and reducing anger and counterarguing. To illuminate the mechanisms underlying the effects of nostalgia, a between-subject experiment (nostalgia appeal vs. regret appeal vs. irrelevant message vs. neutral persuasive message) was conducted among overweight or obese rural Michiganders (N = 507). Results showed that relative to the regret appeal, the nostalgia appeal led to higher state self-esteem, less anger, and less counterarguing. There was no significant difference in attitude or behavioral intention between the nostalgia appeal, regret appeal, and neutral persuasive message. We demonstrated that enhancing self-esteem was the key mechanism by which the nostalgia appeal persuaded the target audience.

Hierarchical sort-based parallel algorithm for dynamic interest matching

Publish–subscribe communication is a fundamental service used for message-passing between decoupled applications in distributed simulation. When abundant unnecessary data transfer is introduced, interest-matching services are needed to filter irrelevant message traffic. Frequent demands during simulation execution makes interest matching a bottleneck with increased simulation scale. Contemporary algorithms built for serial processing inadequately leverage multicore processor-based parallel resources. Parallel algorithmic improvements are insufficient for large-scale simulations. Therefore, we propose a hierarchical sort-based parallel algorithm for dynamic interest matching that embeds all update and subscription regions into two full binary trees, thereby transferring the region-matching task to one of node-matching. It utilizes the association between adjacent nodes and the hierarchical relation between parent‒child nodes to eliminate redundant operations, and achieves incremental parallel matching that only compares changed regions. We analyze the time and space complexity of this process. The new algorithm performs better and is more scalable than state-of-the-art algorithms.

Filtering and Detection of Real-Time Spam Mail Based on a Bayesian Approach in University Networks

With the advent of digital technologies as an integral part of today’s everyday life, the risk of information security breaches is increasing. Email spam, commonly known as junk email, continues to pose a significant challenge in the digital realm, inundating inboxes with unsolicited and often irrelevant messages. This relentless influx of spam not only disrupts user productivity but also raises security concerns, as it frequently serves as a vehicle for phishing attempts, malware distribution, and other cyber threats. The prevalence of spam is fueled by its low-cost dissemination and its ability to reach a wide audience, exploiting vulnerabilities in email systems. This paper marks the inception of an in-depth investigation into the viability and potential implementation of a robust spam filtering and prevention system tailored explicitly to university networks. With the escalating threat of email-based hacking attacks and the incessant deluge of spam, the need for a comprehensive and effective defense mechanism within academic institutions becomes increasingly imperative. In exploring potential solutions, this study delves into the applicability and efficacy of Bayesian filters, a class of probabilistic classifiers renowned for their aptitude in distinguishing between legitimate emails and spam messages. Bayesian filters utilize statistical algorithms to analyze email content, learning patterns and features to accurately categorize incoming emails.

NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification.

Message passing has evolved as an effective tool for designing graph neural networks (GNNs). However, most existing methods for message passing simply sum or average all the neighboring features to update node representations. They are restricted by two problems: 1) lack of interpretability to identify node features significant to the prediction of GNNs and 2) feature overmixing that leads to the oversmoothing issue in capturing long-range dependencies and inability to handle graphs under heterophily or low homophily. In this article, we propose a node-level capsule graph neural network (NCGNN) to address these problems with an improved message passing scheme. Specifically, NCGNN represents nodes as groups of node-level capsules, in which each capsule extracts distinctive features of its corresponding node. For each node-level capsule, a novel dynamic routing procedure is developed to adaptively select appropriate capsules for aggregation from a subgraph identified by the designed graph filter. NCGNN aggregates only the advantageous capsules and restrains irrelevant messages to avoid overmixing features of interacting nodes. Therefore, it can relieve the oversmoothing issue and learn effective node representations over graphs with homophily or heterophily. Furthermore, our proposed message passing scheme is inherently interpretable and exempt from complex post hoc explanations, as the graph filter and the dynamic routing procedure identify a subset of node features that are most significant to the model prediction from the extracted subgraph. Extensive experiments on synthetic as well as real-world graphs demonstrate that NCGNN can well address the oversmoothing issue and produce better node representations for semisupervised node classification. It outperforms the state of the arts under both homophily and heterophily.

Self-effects of user-generated messages on pro-environmental identity

An experiment tested whether user-generated pro-environmental messages (UGC) enhanced environmental self-identity among message creators. Participants were randomly assigned to create a message about saving energy, view a message with similar content ostensibly created by similar others, or view irrelevant messages. Those who created their own messages had greater post-message self-identity than those in the other two conditions. Additionally, those who created their own messages had greater post-message intentions to save energy in the near future than those in the other two conditions, and behavioral intentions were positively correlated with self-identity. Implications for campaigns and educational efforts are discussed.

Targeted multi-agent communication algorithm based on state control

As an important mechanism in multi-agent interaction, communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents. However, the existing communication schemes can bring much timing redundancy and irrelevant messages, which seriously affects their practical application. To solve this problem, this paper proposes a targeted multi-agent communication algorithm based on state control (SCTC). The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard- and self-attention mechanisms, realizing targeted communication message processing. In addition, by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message, the correctness of the final action choice of the agent is ensured. Our evaluation using a challenging set of StarCraft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents, thus ensuring better cooperation between agents.

Reducing COVID-19 vaccine hesitancy among African Americans: the effects of narratives, character's self-persuasion, and trust in science.

This research examines the efficacy of self-persuasion narratives (i.e., narratives that describe how a character has changed their mind about the COVID-19 vaccines) in encouraging vaccine uptake among unvaccinated African Americans. A five-condition experiment (N = 394) was conducted in June 2021. Participants viewed one of the three pro-vaccine messages (a self-persuasion narrative, a narrative without self-persuasion, or a non-narrative message) or an irrelevant message or completed a self-persuasion task. Findings supported the persuasive benefits of the self-persuasion narrative compared to the narrative without self-persuasion, actual self-persuasion, and the irrelevant message. Its advantage over the narrative without self-persuasion was mediated by increased self-referencing, affective empathy, and perceived similarity with the character. Moreover, its psychological effects were moderated by participants’ trust in science. Unexpectedly, the non-narrative showed persuasive benefits compared to other intervention strategies. The theoretical implications for narrative persuasion and practical implications for vaccine promotion were discussed.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10865-022-00303-8.

Heterogeneous Feature Analysis on Twitter Data Set for Identification of Spam Messages

Spam is an undesirable content that present on online social networking sites, while spammers are the users who post this content on social networking sites. Unwanted messages posted on Twitter may have several goals and the spam tweets can interfere with statistics presented by Twitter mining tools and squander users’ attention.. Since Twitter has achieved a lot of attractiveness through-out the world, the interest towards it by the spammers and malevolent users is also increases. To overcome the spam problems many researchers proposed ideas using machine learning algorithms for the identification of spam messages. Not only the selection of classifiers but also the variegated feature analysis is essential for the identification of irrelevant messages in social networks. The proposed model performs a heterogeneous feature analysis on the twitter data streams for classifying the unsolicited messages using binary and continuous feature extraction with sentiment analysis on social network datasets. The features created are assessed using significant stratagems and the finest features are selected. A classifier model is built using these feature vectors to predict and identify the spam messages in Twitter. The experimental results clearly show that the proposed Sentiment Analysis based Binary and Continuous Feature Extraction model with Random Forest (SA-BC-RF) approach classifies the spam messages from the social networks with an accuracy of 90.72% when compared with the other state-of-the-art methods.

Spam Text Detection

Spam Detection is the process to classify text which contains irrelevant or unsolicited messages sent over the internet, typically to a large number of users, for the purposes of advertising, phishing, spreading malware, etc. Text summarization is the technique of converting long text to short. The intention is to make a coherent and fluent summary having only the most points outlined within the document. A USA based machine learning expert which had 13 years of experience and currently teaches people his skills, states his technique has proved to be critical in quickly and accurately summarizing voluminous texts, something which might be expensive and time-consuming if avoided machines. Machine learning models are usually trained to know documents and distil the useful information before outputting the specified summarized texts.

A Comparative Analysis of Machine Learning Techniques for Spam Detection

Internet provides a medium to connect with individuals of similar or different interests creating a hub. Since a huge hub participates on these platforms, the user can receive a high volume of messages from different individuals creating a chaos and unwanted messages. These messages sometimes contain a true information and sometimes false, which leads to a state of confusion in the minds of the users and leads to first step towards spam messaging. Spam messages means an irrelevant and unsolicited message sent by a known/unknown user which may lead to a sense of insecurity among users. In this paper, the different machine learning algorithms were trained and tested with natural language processing (NLP) to classify whether the messages are spam or ham.

A preregistered replication and extension of the cocktail party phenomenon: One's name captures attention, unexpected words do not.

In the cocktail party phenomenon, participants cannot attend to more than 1 stream of information, but sometimes detect their own name being presented in the irrelevant message during a selective listening task. Here we present a preregistered replication of the phenomenon, in which we also tested whether semantically unexpected words have a similar effect and whether individual differences in working memory capacity as measured by the operation and running-span tasks are related to the ability to detect one's own name or unexpected words in the irrelevant message. Twenty-nine percent of the participants reported noticing their own name, and those who did made more errors on relevant, to-be-shadowed words presented around the time of the name. Low-span participants were more likely than high-span participants to notice their names and to commit shadowing errors concurrently to the presentation of the name or shortly after. In contrast, semantically unexpected words were rarely detected, nor were they associated with shadowing errors in the relevant message. Our results demonstrate once again that highly relevant stimuli such as one's own name are capable of attracting and capturing attention for a short period of time. Our results also demonstrate that unexpected words within sentences do not belong to this category of stimuli. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The effects of information relevancy on driving behavior

Bus driving is a complex and cognitively challenging task that places high demands on bus drivers’ working memory. Increasing use of “In-Vehicle Information Systems” leads to driver distraction and is a contributing factor to many road accidents globally, and with systems for tickets, navigation, and timetables, bus drivers are more exposed to this additional workload than other actors in the traffic. This study provides insights into how bus drivers’ driving behavior is affected by auditory traffic information through a driving simulator study at the Swedish National Road and Transport Research Institute. A pre-study showed that many bus drivers in Gothenburg experience that a majority of the messages they receive are irrelevant to them. Difference in driving behavior was identified for conditions in which the drivers received messages irrelevant to their route, which might indicate that irrelevancy is an important factor for the workload imposed to the drivers. We hypothesize that irrelevant messages require processing in the visuospatial sketchpad, which might increase workload more than just auditory information processing. The results of this study implies that the routines for traffic communication between traffic controllers and bus drivers should be considerate, as to reduce the number of irrelevant messages that are cognitively loading the bus drivers.

Inattentional blindness and information relevance of variable message signs

It has been suggested that Variable Message Signs (VMS) become less effective at communicating important traffic information when irrelevant information is also displayed on them. The purpose of this study was to examine if practice reading irrelevant information on a VMS influenced compliance with, and memory for a detour message. Thirty-nine participants were randomly assigned to one of three groups who drove a simulated road; one receiving only a detour message on the VMS, one group received irrelevant VMS messages before the detour message, and a third group received the same messages but the detour message was inconsistent with their destination. Of interest were both the participants’ compliance with the target detour message, as well as their later recall and recognition of the message. The results suggested that, first and foremost, there was significantly lower compliance with the detour message when it had been preceded by irrelevant messages on the VMS. All of the groups showed reasonably good memory for the detour message. The implications of the present study are that presentation of irrelevant messages, including advertisements and safety slogans, may result in reduced compliance to traffic-relevant messages on VMSs.

English

The study was initiated to assess the attitude towards mobile marketing among students of Bahir Dar University, Ethiopia. The objective of the study is to evaluate whether the university students have accepted Mobile/SMS advertising positively and to understand the issues students experienced and the thoughts they perceived from educational perspective considering different factors affecting their attitude towards Mobile/SMS advertising. Data were collected from 98 students using convenience sampling method. The major results of the study indicated students do not have a strong positive attitude towards mobile advertising and they believe that it has a negative factor on quality of education. The study recommended the mobile marketers to properly segment their customers in order to stop irrelevant messages being sent and ethio–telecom to start to apply permission-marketing in order to protect its customers from disturbance. Key words: Mobile advertising, mobile marketing, permission marketing, Bahir Dar University.

Rapid relevance classification of social media posts in disasters and emergencies: A system and evaluation featuring active, incremental and online learning

The research field of crisis informatics examines, amongst others, the potentials and barriers of social media use during disasters and emergencies. Social media allow emergency services to receive valuable information (e.g., eyewitness reports, pictures, or videos) from social media. However, the vast amount of data generated during large-scale incidents can lead to issue of information overload. Research indicates that supervised machine learning techniques are suitable for identifying relevant messages and filter out irrelevant messages, thus mitigating information overload. Still, they require a considerable amount of labeled data, clear criteria for relevance classification, a usable interface to facilitate the labeling process and a mechanism to rapidly deploy retrained classifiers. To overcome these issues, we present (1) a system for social media monitoring, analysis and relevance classification, (2) abstract and precise criteria for relevance classification in social media during disasters and emergencies, (3) the evaluation of a well-performing Random Forest algorithm for relevance classification incorporating metadata from social media into a batch learning approach (e.g., 91.28%/89.19% accuracy, 98.3%/89.6% precision and 80.4%/87.5% recall with a fast training time with feature subset selection on the European floods/BASF SE incident datasets), as well as (4) an approach and preliminary evaluation for relevance classification including active, incremental and online learning to reduce the amount of required labeled data and to correct misclassifications of the algorithm by feedback classification. Using the latter approach, we achieved a well-performing classifier based on the European floods dataset by only requiring a quarter of labeled data compared to the traditional batch learning approach. Despite a lesser effect on the BASF SE incident dataset, still a substantial improvement could be determined.

An exponential search enhanced dynamic sort-based interest matching algorithm for interest management in distributed simulation

Interest management is employed in most distributed simulation to filter irrelevant messages on network and is essential for the performance and scalability of simulation systems. In High-Level Architecture (HLA), the data distribution management (DDM) adopts an interest matching algorithm to filter unnecessary communications among federates by computing the overlap statuses between the update and subscription regions. In many spatial applications, only a portion of regions changes gradually at each time step. Hence, dynamic matching for selective modified regions is necessary. The dynamic sort-based matching algorithm has proved to be the most efficient. However, the existing dynamic sort-based matching algorithms do not separate modified regions from static regions, which will produce large amounts of unnecessary comparisons and swap operations between modified and static regions. To solve the problem, we propose an exponential search enhanced dynamic sort-based interest matching algorithm called EDSIM. In EDSIM, (1) the modified regions are separated from static regions; thus, the latter are independent from the sorting operations; (2) an exponential search method is adopted to calculate the rechecking sets for modified regions, reducing unnecessary bound comparisons between modified and static regions; (3) a bubble-sort-based matching algorithm is used to update the overlap statuses between modified regions. Experimental results demonstrate that the EDSIM algorithm achieves significantly better performance than the major existing dynamic matching algorithms under various experimental scenarios.

RETRACTED ARTICLE: Classifying streaming of Twitter data based on sentiment analysis using hybridization

Twitter is a social media that developed rapidly in today’s modern world. As millions of Twitter messages are sent day by day, the value and importance of developing a new technique for detecting spammers become significant. Moreover, legitimate users are affected by means of spams in the form of unwanted URLs, irrelevant messages, etc. Another hot topic of research is sentiment analysis that is based on each tweet sent by the user and opinion mining of the customer reviews. Most commonly natural language processing is used for sentiment analysis. The text is collected from user’s tweets by opinion mining and automatic sentiment analysis that are oriented with ternary classifications, such as “positive,” “neutral,” and “negative.” Due to limited size, unstructured nature, misspells, slangs, and abbreviations, it is more challenging for researchers to find sentiments for Twitter data. In this paper, we collected 600 million public tweets using URL-based security tool and feature generation is applied for sentiment analysis. The ternary classification is processed based on preprocessing technique, and the results of tweets sent by the users are obtained. We use a hybridization technique using two optimization algorithms and one machine learning classifier, namely particle swarm optimization and genetic algorithm and decision tree for classification accuracy by sentiment analysis. The results are compared with previous works, and our proposed method shows a better analysis than that of other classifiers.

Verifying Safety Messages Using Relative-Time and Zone Priority in Vehicular Ad Hoc Networks.

In high-density road networks, with each vehicle broadcasting multiple messages per second, the arrival rate of safety messages can easily exceed the rate at which digital signatures can be verified. Since not all messages can be verified, algorithms for selecting which messages to verify are required to ensure that each vehicle receives appropriate awareness about neighbouring vehicles. This paper presents a novel scheme to select important safety messages for verification in vehicular ad hoc networks (VANETs). The proposed scheme uses location and direction of the sender, as well as proximity and relative-time between vehicles, to reduce the number of irrelevant messages verified (i.e., messages from vehicles that are unlikely to cause an accident). Compared with other existing schemes, the analysis results show that the proposed scheme can verify messages from nearby vehicles with lower inter-message delay and reduced packet loss and thus provides high level of awareness of the nearby vehicles.

Detecting Streaming of Twitter Spam Using Hybrid Method

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work.

Semantic Analysis of Open Source Data for Syndromic Surveillance

ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p<0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events.