Message Retransmission Research Articles

The what and how of warning messages: An experimental examination of instructing information and linguistic features use

AbstractWe conducted a between‐subjects experiment (N = 1626) to examine the effects of instructing information, an important theoretical keystone in crisis communication research, in predicting two key outcomes in the literature—protective behavioral intentions and message retransmission intentions. Given the communicative context, high‐impact weather warnings, we additionally considered the moderating role of linguistic features use in predicting these outcomes. Based upon theory and research in this area, we examined the mediating roles of self‐efficacy and perceived instructing information in predicting protective behavioral intentions and message retransmission intentions, respectively. Results mainly support our hypotheses. The instructing information conditions predicted the proposed intervening variable, which in turn predicted both outcomes. Linguistic features use moderated the direct effect of instructing information in predicting protective behavioral intentions, but not message retransmission intentions. The discussion focuses on the implications of these findings for crisis communication theory and practice.

Coping with Unknown Health Crisis via Social Media: A Content Analysis of Online Mutual Aid Group in the Beginning of the COVID-19 Pandemic.

The initial emergence of SARS-CoV-2 created uncertainty for humanity, driving people to seek assistance on social media. This study aims to understand the role of social media in coping with crises and to offer guidance for future uncertainties by examining the experiences of Wuhan during the early stages of the pandemic. Using quantitative content analysis, this study investigated 2207 Weibo posts tagged with "COVID-19 Mutual Aid" from individuals located in Wuhan during the early lockdown period from January 23, 2020, to March 23, 2020. At the start of pandemic, messages seeking tangible support were most common. A hurdle regression model showed that deeper self-disclosure led to more retransmission of help-seeking messages. The Chi-Square and Mann-Whitney U tests revealed that health professionals and laypeople had different self-disclosure strategies. This study provides insight into the online social support exchange during the early stages of the COVID-19 pandemic in Wuhan, highlighting the importance of self-disclosure on message retransmission, and the differences in self-disclosure strategies between health professionals and laypeople in online help-seeking.

#Putkids1st: Health Professionals Using Social Media for Public Policy Advocacy-From Collective Action to Connective Action.

This study examines public policy advocacy by pediatricians and other health professionals in the hashtag community: #putkids1st. The study explores 4321 tweets that feature the hashtag, generated by 1231 unique users largely drawn from the American Association of Pediatricians and its members. The data are used to explore the structural dynamics of the hashtag community, the role of homophily, and to test a source-message framework to predict and recommendations to help improve engagement and retransmission of professional health advocacy messages.

Radical Mobilization in Times of Crisis: Use and Effects of Appeals and Populist Communication Features in Telegram Channels

Social crisis situations such as the COVID-19 pandemic provide a fertile ground for radical actors and social movements to strengthen their radical mobilization—mainly using social media. In light of the deplatforming efforts of social media platforms that forced radical actors to establish new channels to continue their discourses and the recent crisis that opened a discursive opportunity structure, we investigate how radical actors use Telegram for mobilization. Based on a longitudinal manual content analysis of 13,371 messages from 188 German Telegram channels of the Querdenken movement, conspiracy, and far-right actors, we examine mobilization strategies (namely direct appeals and populist blame game) and their success (retransmission) between March 2020 and December 2021. We found that both offline and online appeals and the use of anti-elitism increased significantly over time. The use of direct and indirect mobilization attempts is related to the ideological background of the actors, with direct appeals being favored by the Querdenken movement. Furthermore, the results shed light on the mechanisms of radical mobilization on Telegram, showing that both direct appeals and the populist blame game are successful strategies for radical actors because they increase message retransmission.

A Hybrid Optimization Solution for UAV Network Routing

An Unmanned Aerial Vehicle (UAV) network specifies a novel type of Mobile Ad hoc Network (MANET) in which drones serve as nodes and facilitate the retransmission of messages to their final destinations. Aside from its military application, it has recently begun to seep into the civilian sector. Similar to MANET and vehicular ad hoc networks, Flying Ad hoc Networks (FANET) are a subset of ad hoc networks. An FANET is different because it is founded on UAVs. Due to the characteristics of this sort of network, which is defined by a highly changing topology in a 3D environment, we must employ an adjusted configuration method to ensure good routing performance. Therefore, to deal with this problem, a technique that responds to any change in topology by always finding the best route is required. In this work, we propose a new protocol based on the hybrid optimization of the 2-opt heuristic and Honey Badger Algorithm (HBA), called HB-AODV. In order to locate its prey, a badger must move slowly and continuously while using scent markers and mouse-digging skills to catch it. In other words, the most efficient routes in terms of the number of hops are identified. Several simulations were conducted via the 3D version of Network Simulator (NS-2) on different deployment strategies. In comparison to AODV, DSDV, and AntHocNet, the obtained results demonstrated the proposed scheme’s good performance in terms of quality of service metrics.

VP-CAST: Velocity and Position-Based Broadcast Suppression for VANETs

In the vehicular ad hoc networks (VANETs), minimizing the broadcast storm that arises due to message rebroadcast during emergency message dissemination in extremely mobile environments under sparse or dense networks is a significant challenge. Proper selection of rebroadcasting vehicles guarantees acceptable end-to-end delay, high delivery ratio, and efficient bandwidth utilization. To date, many protocols have been proposed to select an appropriate rebroadcasting vehicles based on vehicle position information only. However, such approaches neglect the fact that both vehicle velocity and position information can be utilized efficiently to alleviate rebroadcast message collisions and control bandwidth consumption. In this work, we present a new broadcast suppression protocol, named, velocity and position-based broadcast suppression for VANETs (VP-CAST), which can work in both sparse and dense network situations. VP-CAST does rely on periodic beacon messages, rather the position and velocity information of broadcasting vehicle are included in a broadcast message. Moreover, the transmission range of broadcasting vehicle is divided into dynamic time slots based on velocity and position information of broadcasting and receiving vehicles.The proposed scheme assigns shorter and dynamic waiting time to the vehicles moving at high velocities and located farther from the sender vehicle that eventually reduces both the message re-transmission delay and the number of rebroadcasting vehicles. The proposed protocol is compared with the DV-CAST in terms of end-to-end delay, message delivery ratio, and message overhead.

Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks.

In a wireless sensor network (WSN), reducing the energy consumption of battery-powered sensor nodes is key to extending their operating duration before battery replacement is required. Message bundling can save on the energy consumption of sensor nodes by reducing the number of message transmissions. However, bundling a large number of messages could increase not only the end-to-end delays and message transmission intervals, but also the packet error rate (PER). End-to-end delays are critical in delay-sensitive applications, such as factory monitoring and disaster prevention. Message transmission intervals affect time synchronization accuracy when bundling includes synchronization messages, while an increased PER results in more message retransmissions and, thereby, consumes more energy. To address these issues, this paper proposes an optimal message bundling scheme based on an objective function for the total energy consumption of a WSN, which also takes into account the effects of packet retransmissions and, thereby, strikes the optimal balance between the number of bundled messages and the number of retransmissions given a link quality. The proposed optimal bundling is formulated as an integer nonlinear programming problem and solved using a self-adaptive global-best harmony search (SGHS) algorithm. The experimental results, based on the Cooja emulator of Contiki-NG, demonstrate that the proposed optimal bundling scheme saves up to 51.8% and 8.8% of the total energy consumption with respect to the baseline of no bundling and the state-of-the-art integer linear programming model, respectively.

Optimized software-defined multimedia framework: networking and computing resource management

Today, the multimedia over IP (MoIP) network has become a cost-effective and efficient alternative to the public switched telephone network (PSTN). Free applications for multimedia transmission over the Internet have become increasingly popular, gaining considerable popularity around the world. This communication consists of two phases, i.e., signaling phase and media exchange phase. The SIP protocol is responsible for the MoIP network signaling to provide services such as VoIP, voice and video conferencing, video over demand (VoD), and instant messaging. This application layer protocol has been standardized by the IETF for initiating, managing, and tearing down multimedia sessions and has been widely used as the main signaling protocol on the Internet. The signaling and media are handled by SIP proxies and network switches, respectively. One of the most critical challenges in MoIP is the overloading of SIP proxies and network switches. Because of these challenges, a wide range of network users experiences a sharp drop in service quality. Overload occurs when there are not enough processing resources and memory to process all the messages due to the lack of proper routing. This study aims to model the routing problem in MoIP by providing a framework based on software-defined networking (SDN) technology and a convex mathematical programming model to prevent overload. The proposed framework is simulated and implemented using various scenarios and network topologies. The results show that throughput, latency, message retransmission rate, and resource consumption have improved using the proposed approach.

A batch delivery mechanism of network update in software‐defined wide area networks

SummaryIn software‐defined network (SDN) architecture, the network decisions are all located on a logically centralized controller. Based on installing rules into switches of the data plane, the controller directly initiates and schedules a network update and which will cause a delay in the completion time of a network update. The situation is aggravated in wide area networks (WANs) further due to the switches deployed in a wide geographical area bring long propagation delay. On account of adopting the in‐band transmission style to deliver control messages instead of dedicated control channels is economics in WANs, the retransmission of control messages causing by congestion of the data channels is another unbearable delay source. This paper describes a new control information batch delivery mechanism (CIBDM) of network updates to relieve the delay and enhance the reliability of control message transmission in SD‐WANs. The characteristics of the novel scheme include the following: (1) every control information message may contain multiple update information of different forwarding devices, and each update information consists of a set of update instructions with dependencies; (2) by using a similar mechanism of onion routing, in the delivering process of control messages, it attains the security for each control rule of the specified devices; (3) with delegating the update consistency coordination into the data plane, it improves network update on the completion time. A comprehensive evaluation runs on 10 network topologies selected from all over the world. Experimental results show that CIBDM achieves rapid network updates with reliable message transmission.

The "Parallel Pandemic" in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media.

Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.

Cutting Through the Noise: Predictors of Successful Online Message Retransmission in the First 8 Months of the COVID-19 Pandemic.

In this paper, we investigate how message construction, style, content, and the textual content of embedded images impacted message retransmission over the course of the first 8 months of the coronavirus disease 2019 (COVID-19) pandemic in the United States. We analyzed a census of public communications (n = 372,466) from 704 public health agencies, state and local emergency management agencies, and elected officials posted on Twitter between January 1 and August 31, 2020, measuring message retransmission via the number of retweets (ie, a message passed on by others), an important indicator of engagement and reach. To assess content, we extended a lexicon developed from the early months of the pandemic to identify key concepts within messages, employing it to analyze both the textual content of messages themselves as well as text included within embedded images (n = 233,877), which was extracted via optical character recognition. Finally, we modelled the message retransmission process using a negative binomial regression, which allowed us to quantify the extent to which particular message features amplify or suppress retransmission, net of controls related to timing and properties of the sending account. In addition to identifying other predictors of retransmission, we show that the impact of images is strongly driven by content, with textual information in messages and embedded images operating in similar ways. We offer potential recommendations for crafting and deploying social media messages that can “cut through the noise” of an infodemic.

Efficiency of Retransmission in Sensor Networks

We consider a wireless sensor network (WSN) intended for periodic collection of data from sensors. Loss of messages is allowed. A protocol with retransmission of messages is proposed to minimize the loss of individual sensor readings. The protocol has been tested on a benchtop WSN constructed using Arduino Nano microcontroller boards and NRF24L01 transceivers. Natural experiments have shown that the proposed protocol indeed ensures a low frequency of lost messages in the collected data.

Mitigating message dissemination issues in safety applications for vehicular ad hoc networks

SummaryIn a vehicular ad hoc network (VANET) each node is required to transmit periodic and event‐driven messages. The periodic messages are also known as beacons and consist of the basic vehicle status, such as position, speed, direction, and any additional information required by a safety application. One of the main concerns faced with this technology is channel congestion, which often happens in scenarios with a high vehicular density like traffic jams. When the channel is congested, there are no guarantees for a reliable message delivery. For this purpose, several protocols have been proposed to resolve this issue. In this work we propose neighbor congestion avoidance protocol (NCAP) as a novel solution employing a proactive strategy with vehicle position prediction using Kalman filter and an efficient event‐driven message delivery scheme. When compared with other strategies in congested scenarios, NCAP was able to significantly decrease the number of beacons by 49%, while also reducing the number of event‐driven message retransmissions by approximately 81%. NCAP also presented better performance in regard to the number of collisions between messages and channel busy ratio.

COVID-19: Retransmission of official communications in an emerging pandemic.

As the most visible face of health expertise to the general public, health agencies have played a central role in alerting the public to the emerging COVID-19 threat, providing guidance for protective action, motivating compliance with health directives, and combating misinformation. Social media platforms such as Twitter have been a critical tool in this process, providing a communication channel that allows both rapid dissemination of messages to the public at large and individual-level engagement. Message dissemination and amplification is a necessary precursor to reaching audiences, both online and off, as well as inspiring action. Therefore, it is valuable for organizational risk communication to identify strategies and practices that may lead to increased message passing among online users. In this research, we examine message features shown in prior disasters to increase or decrease message retransmission under imminent threat conditions to develop models of official risk communicators' messages shared online from February 1, 2020-April 30, 2020. We develop a lexicon of keywords associated with risk communication about the pandemic response, then use automated coding to identify message content and message structural features. We conduct chi-square analyses and negative binomial regression modeling to identify the strategies used by official risk communicators that respectively increase and decrease message retransmission. Findings show systematic changes in message strategies over time and identify key features that affect message passing, both positively and negatively. These results have the potential to aid in message design strategies as the pandemic continues, or in similar future events.

An Achievability Bound of Energy Per Bit for Stabilized Massive Random Access Gaussian Channel

Developing communication standards highlight a scenario called massive Machine-Type Communication within the framework of the Internet of Things. Under this scenario, a large number of devices with autonomous power supplies will operate in a random multiple access mode. It is crucial to keep the system stable, while consuming minimal energy. We describe a model of a system with a Gaussian Random Access Channel and consider an algorithm allowing message retransmissions. We provide a stability condition for the system and introduce an optimization problem to determine an achievability bound for energy per bit.

Effects of Opinion Climate, Efficacy Messages, and Publicness of Social Media on Intentions to Retransmit Anti-Binge Drinking Messages on Facebook.

Reducing the rate of college binge drinking is a major public health goal. Social media sites such as Facebook serve as platforms for young adults' online communication, so they could potentially extend the reach of health campaign messages via retransmission. Thus, it is important to identify the factors that predict intentions to share health-related messages on social media. Drawing on the Spiral of Silence framework, the current research examined the effects of opinion climate, message efficacy, and publicness of social media on message retransmission intentions for anti-binge drinking "how-to" messages-messages that include advice and recommendations that target beliefs about efficacy. A 2 × 2 × 2 (efficacy: high vs. low × opinion climate: support vs. oppose × channel: public vs. private) between-subjects experiment was conducted. Data from 245 participants on Amazon Mechanical Turk demonstrated that people were more willing to share when they had an anti- rather than a pro-binge drinking opinion climate, when the messages were more useful, and when they were asked to share via private messaging rather than via public status updates. Theoretical and practical implications for the psychological mechanisms underlying message retransmission on social media are discussed.

An Examination of the Effects of Employee Words in Organizational Crisis: Public Forgiveness and Behavioral Intentions

This study examines how employees’ messages about a corporate crisis affect external publics’ attitudes and behaviors toward an organization. Specifically, the study investigates whether the valence (positive vs. negative) and communication channels (face-to-face vs. social media) of employees’ messages interact with each other. The results of an online experiment indicate that negative messages concerning an organizational crisis significantly affect publics’ perception of message credibility, behavioral forgiveness, and message retransmission intentions when the messages were delivered during face-to-face communication, while negative messages on social media do not significantly affect publics’ attitudes and behaviors. Positive messages had almost equal impacts on the outcomes, regardless of the communication channels through which the message was shared. Theoretical and practical implications for corporate and business communication are discussed.

Reason and passion in public discussion on Sina Weibo

This study aimed to explore the relationship between reason and passion on the Chinese microblogging site Weibo, with an emphasis on the coexistence of emotional and rational arguments as well as how they predict message retransmission. It found that public discussions on the microblogging site are mostly emotional. However, advanced reasoning was also present in emotional discussions, which manifested itself in making appeals to the common good and using logical coherence. Although emotion and rationality are not mutually exclusive, they do produce different retransmission outcomes, with emotion positively related to reposts and the appeal to the common good receiving fewer shares.

Design and performance evaluation of a LoRa-based mobile emergency management system (LOCATE)

Smartphone devices can play a key role on emergency scenarios thanks to their pervasiveness, and the possibility to convey emergency requests from the involved people to the rescue teams. At the same time, the effective utilization of such devices on critical scenarios with limited mobile Internet access is challenging. As an alternative, several recent research studies have proposed Emergency Communication System (ECS) based on short-range Device-to-Device (D2D) solutions available on Commercial Off The Shelf (COTS) devices (e.g. Wi-Fi Direct); however, the target of these solutions is constituted by small indoor areas, since the scalability on large-scale environments is often a problem. In this paper, we overcome such issue by proposing LOCATE, a novel phone-based ECS enabling long-range communication among survivors and rescue teams over critical environments where 3/4G cellular connectivity is not available and the traditional geo-localization technologies (e.g. the GPS) provide only partial coverage of the environment. The proposed system consists of a mobile application connected to a LoRa transceiver via Bluetooth Low Energy (BLE); through the app, users can send emergency requests that are re-broadcasted by other peers until reaching a rescue personnel who is able to handle the emergency. Three novel contributions are provided in this paper. First, we provide extensive measurements of the LoRa technology, and investigate its suitability for ECS-related applications. Second, we describe the LOCATE prototype and the enabling algorithms; specifically, we propose a novel multi-hop dissemination algorithm which maximizes the probability to deliver an emergency request to the destination (e.g. rescue personnel) within a user-defined temporal threshold, while minimizing the number of message re-transmissions. Third, we extensively evaluate the LOCATE performance through OMNeT++ simulations, assessing the capability of the dissemination protocol to spread out the emergency requests over large-scale scenarios, and through experiments, assessing the capability of LoRa-based trilateration technique to provide accurate GPS-free localization. The results demonstrate that the LOCATE protocol is able to minimize the time required to handle the emergency when compared to other dissemination strategies (e.g. flooding, continuous, once-per-contact), and they highlight the considerable improvement provided by the LoRa technology over other D2D solutions available on COTS smartphones (e.g. Wi-Fi Direct).

Getting the Word Out, Rain or Shine: The Impact of Message Features and Hazard Context on Message Passing Online

AbstractNetworked social media provide governmental organizations, such as the National Weather Service (NWS), the opportunity to communicate directly with stakeholders over long periods of time as a form of online engagement. Typologies of engagement include aspects of message content that provide information, contribute to community building, and inspire action and aspects of message microstructural features that facilitate interaction and dialogue, such as directed messages, hashtags, and URLs. Currently, little is known regarding the effect of message strategies on behavioral outcomes, and whether those effects vary under different weather conditions. In this paper we examine how message practices used on Twitter by the NWS are related to message engagement under routine and nonroutine weather conditions. Our analysis employs a census of tweets sent by 12 NWS Weather Forecast Offices in spring 2016 and uses a combination of manual and automated coding to identify engagement content and microstructure features present in each message. We identify factors that increase and decrease message retransmission (retweets) within this corpus under varying threat conditions, using a mixed-effects negative binomial regression model. We find that inclusion of actionable message content, information about historical weather facts, attached visual imagery (such as a map or infograph), and named event hashtags increases message passing during both threat and nonthreat periods. In contrast, messages that include forecast and nowcast content and messages that are sent in reply to other users have a lower passing rate. Findings suggest that common message features do alter message passing, potentially informing message design practices aimed at increasing the reach of messages sent under threat conditions.