Short of Suspension: How Suspension Warnings Can Reduce Hate Speech on Twitter

Debates around the effectiveness of high-profile Twitter account suspensions and similar bans on abusive users across social media platforms abound. Yet we know little about the effectiveness of warning a user about the possibility of suspending their account as opposed to outright suspensions in reducing hate speech. With a pre-registered experiment, we provide causal evidence that a warning message can reduce the use of hateful language on Twitter, at least in the short term. We design our messages based on the literature on deterrence, and test versions that emphasize the legitimacy of the sender, the credibility of the message, and the costliness of being suspended. We find that the act of warning a user of the potential consequences of their behavior can significantly reduce their hateful language for one week. We also find that warning messages that aim to appear legitimate in the eyes of the target user seem to be the most effective. In light of these findings, we consider the policy implications of platforms adopting a more aggressive approach to warning users that their accounts may be suspended as a tool for reducing hateful speech online.

COVID-19: Role of Deep Learning and Cloud Through Identification of Kidney, Pancreas and Intestine

Worldwide human health and economic has been affected due to the ongoing pandemic of corona virus (COVID-19). The major COVID-19 challenges are prevention, monitoring and FDA approved vaccines. IOT and cloud computing play vital role in epidemic prevention and blocking COVID-19 transmission. Mostly lungs and hearts are affected. Other than lungs many parts are affected which are not considered as prominent conversational cue. In this paper, we have proposed smart system that is effective through detection of pancreas, kidney and intestine. It detects acute pancreatitis, protein leak, microscopic blood leak, post infectious dysmotility and gastrointestinal bleeding. The data from the edge devices are collected and mapped into the cloud layer. The cloud consists of COVID-19 patients medical records which compare the user data with the existing patient records. Once the data matches it sends warning message to the user regarding the result of affected parts. Based on the result from KPI system, it analyzes with all data and using deep Convolutional Neural Network (CNN) it classifies whether the pancreas, kidney and intestine are affected or not due to COVID-19.

Chat Tracking and Monitoring System

With the expanding utilization of instant chat messengers to share data, Suspicious exercises have additionally expanded. There are numerous sources to share the data however moment talk couriers and informal communication sites are fast, simply intended to share anything. In some cases, even new stories are at first separated via online media locales and further on talk couriers rather than any news channel and paper and so forth. Because of these innovation progressions, a few individuals are using these applications inappropriately to share suspicious messages and make arrangements to accomplish something unlawful. With the headway of web innovation and the change in the method of correspondence, it is discovered that much direct news has been examined in Internet discussions well before they are accounted for in conventional broad communications. Additionally, this correspondence channel gives a viable channel to criminal operations, for example, communicating of copyrighted films, compromising messages and internet betting and so on. Our Proposed Framework will examine online plain text messages from chosen conversation gatherings and our framework will choose which post is legitimate and unlawful. It will detect the suspicious keyword from the chatting and give a warning message to the user and also a mail will be sent to admin which will include all the details of the user. Keywords: Suspicious Chat, Chat Monitoring, Abusive word, Terrorist activities, Chat Tracking.

A Practical Method of Optimal RSUs Deployment for Smart Internet of Vehicle

The roadside units (RSUs) are absolutely indispensable elements in sparse highway senario for smart Internet of Vehicle (IoV). Most recent RSU deployment methods consider vehicle mobility, warning message reception probability and time delay respectively, do not mentioned the situation once the accident vehicle cant send the warning message. In this paper, we present a comprehensive analysis that integrates all these factors above. In particular, we model three kinds of common highway scenarios and give the closed-form expression of RSUs deployment number along the highway. The proposed method has been validated by extensive simulations using Matlab and NS2, its performance has been compared with TAPC method. Results reveal that our proposed method has better performance under the condition of high warning message probability.

A Walk in the Park? Examining the Impact of App-Based Weather Warnings on Affective Reactions and the Search for Information in a Virtual City

Background: Warning apps can provide personalized public warnings, but research on their appraisal and impact on compliance is scarce. This study introduces a virtual city framework to examine affective reactions when receiving an app-based warning, and subsequent behavioral intentions. Methods: In an online experiment, 276 participants (M = 41.07, SD = 16.44, 62.0% female) were randomly allocated to one of eight groups (warning vs. no warning, thunderstorm vs. no thunderstorm, video vs. vignette). Participants were guided through a virtual city by a mock-up touristic app (t1). Then, the app issued a warning about an impending thunderstorm (t2), followed by a virtual thunderstorm (t3). The virtual city tour was presented via vignettes or videos. ANCOVAs were used to investigate trajectories of momentary anxiety, hierarchical regressions analyzed the impact of momentary anxiety on information seeking. Results: Participants who received a warning message and were confronted with a thunderstorm showed the highest increase in momentary anxiety, which predicted information seeking intentions. Conclusions: The findings underscore the importance of affective appraisal in processing warning messages. The virtual city framework is able to differentiate the impact of warning versus event in an online context, and thus promising for future warning research in virtual settings.

Health Information on Waterpipe Lounge Menus to Educate Young Adults: Pilot Study Findings

Background For years, tobacco risk communication has largely focused on cigarette smoking. New strategies must be developed to adapt to emerging tobacco products, such as waterpipe tobacco smoking (WTS). Aims The purpose of this pilot study was to determine the preliminary effects of health information on waterpipe lounge menus on the perceptions of harm and risk from WTS and inform future efficacy interventions for health communication (i.e., educating populations on the risks, harms, and health consequences of WTS). Method Participants aged 18 to 24 years ( n = 213) who smoked waterpipe at least monthly were randomized to one of four waterpipe lounge menu groups using a two-by-two experimental design with “warning message” and “nicotine content” as factors. Results Those who viewed waterpipe lounge menus that included a warning message had greater perceived relative harm to health and perceived risk of decreased lung function from WTS. Those who viewed waterpipe lounge menus that included nicotine content had greater perceived risk of heart attack from WTS. Discussion Participants who were exposed to health warnings of WTS and information on the nicotine content of waterpipe tobacco increased on measures of perceived relative harm and risk of health consequences. Conclusion The pilot test results indicate promise for providing health information on waterpipe lounge menus to educate young adults on the harms and risks of WTS.

How do different elements of a warning message influence spontaneous reaction to the warning and intended behavioural change? A factorial survey experiment

<p>Weather warnings serve the purpose of informing the public about potentially dangerous weather events so that they can take precautionary measures to avoid harm and damages. However, weather warning are often not user-oriented, which leads to poor understanding and low compliance rate. Moreover, warnings are often received during daily activities when the decision whether to respond to the warning might be taken within only a few seconds. The present study focuses on the question, which elements of a warning message are the most important to influence the spontaneous reaction to the warning and the intention to take action.</p><p>In a factorial survey experiment with 2000 Swiss citizen, we tested the influence of different elements of a warning message on people’s spontaneous appraisal of the warning and their intended behavioural change. The elements of the warning message we tested for were physical values (e.g. amount of rain in mm.), impact information, behavioural recommendations, warning level and labels for the severity of the event (e.g. “very severe”). We used an implicit association test to measure spontaneous appraisal of the warning message with respect to understanding, trust, risk perception and personal relevance. After the implicit association test, participants explicitly answered whether they would change their behaviour in response to the warning.</p><p>The experimental setup allows us to test for causal relations between the different elements of the warning message and the spontaneous reaction and intended behavioural response. Measuring the implicit associations provides us with a better understanding of the first reactions triggered by the warning elements and how that impacts intended behavior.</p><p>Our results (available by the end of April) will shed light on the question which information is the most important to serve as a wake-up call – a question that becomes even more relevant as warnings are increasingly transmitted via push-notifications on mobile phones. At the same time, our study provides a further insight into the cognitive process that underlies the decision to take protective actions.</p>

Spotted Hyena Optimization and Simulated Annealing-based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs

An accurate localization technique is considered as the significant entity in Vehicular Ad hoc Networks (VANETs) for facilitating emergency message data transmission in diversified critical safety applications. In VANETs, the system of global positioing is generally used for estimating the position of the vehicles in the network for attaining neighborhood awareness in the event of warning message dissemination. However, the existence of green foliages, buildings, indoor parking lots and urban streen canyons introduces NLOS situation that introduces unwanted errors that crumbles the degree of data dissemination in emergency situations. In this paper, Spotted Hyena and Simulated Annealing Optimization Algorithm (SHSAOA)-based positioning scheme was proposed for precise estimation of NLOS nodes. İt included the advantages of improved simulated annealing (SA) integrated into SHOA for establishing better balance between the process of exploitation and exploration in the search space. This positioning approach generated candidate solutions by deriving the merits of the trajectory-based charateristics of SA throughout the algorithmic development process in order to improve the local optimization process. This proposed SHSAOA utilized the distance infotmation that are associated with the vehicle trajectory, number of vehicles and error in distance information for assessing the precise location of the NLOS nodes in the network. The simulation results of the proposed SHSAOA scheme confirmed minimized localization error with maximized accuracy in transmission, warning message transmission rate, channel utilization degree and neighborhood awareness degree with different vehicular density and NLOS nodes.

Effects of Automated Messages on Internet Users Attempting to Access “Barely Legal” Pornography

With the increasing number of individuals accessing online child sexual exploitation material (CSEM), there is an urgent need for primary prevention strategies to supplement the traditional focus on arrest and prosecution. We examined whether online warning messages would dissuade individuals from visiting a honeypot website purporting to contain barely legal pornography. Participants ( n = 419) seeking the site were randomly assigned to one of five conditions; they went straight to the landing page (control; n = 100) or encountered a warning message advising of the potential harm to viewers ( n = 74), potential harm to victims ( n = 65), ability of police to track IP addresses ( n = 81), or possible illegality of such pornography ( n = 99). We measured the attempted click-through to the site. Attrition rates for the warning message conditions were 38% to 52%, compared with 27% for the control group. The most effective messages were those that warned that IP addresses can be traced (odds ratio [OR] = 2.64) and that the pornography may be illegal (OR = 2.99). We argue that warning messages offer a valuable and cost-effective strategy that can be scaled up to help reduce the accessing of CSEM online.