#Outage: Detecting Power and Communication Outages from Social Networks

Paul, Udit; Ермаков, А. Н.; Nekrasov, Michael; Adarsh, Vivek; Belding, Elizabeth

doi:10.1145/3366423.3380251

Cited by 20 publications

(4 citation statements)

References 17 publications

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“…For instance, given the increased load on video streaming platforms during COVID-19 [50], cellular operators could employ our approach to detect sectors with possible bottlenecks without having to rely on user feedback/complaints, particularly in remote locations. This has the potential to lead to faster turnaround times for network troubleshooting [55], and therefore may lower outage periods for users heavily dependent on video streaming.…”

Section: Resultsmentioning

confidence: 99%

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Adarsh

Nekrasov

Paul

et al. 2021

Passive and Active Measurement

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The explosion of mobile broadband as an essential means of Internet connectivity has made the scalable evaluation and inference of quality of experience (QoE) for applications delivered over LTE networks critical. However, direct QoE measurement can be time and resource intensive. Further, the wireless nature of LTE networks necessitates that QoE be evaluated in multiple locations per base station as factors such as signal availability may have significant spatial variation. Based on our observations that quality of service (QoS) metrics are less time and resource-intensive to collect, we investigate how QoS can be used to infer QoE in LTE networks. Using an extensive, novel dataset representing a variety of network conditions, we design several state-of-the-art predictive models for scalable video QoE inference. We demonstrate that our models can accurately predict rebuffering events and resolution switching more than 80% of the time, despite the dataset exhibiting vastly different QoS and QoE profiles for the location types. We also illustrate that our classifiers have a high degree of generalizability across multiple videos from a vast array of genres. Finally, we highlight the importance of low-cost QoS measurements such as reference signal received power (RSRP) and throughput in QoE inference through an ablation study.

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Section: Resultsmentioning

confidence: 99%

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Adarsh

Nekrasov

Paul

et al. 2021

Passive and Active Measurement

Self Cite

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“…Centres around distinguishing blackouts involving tweets as data source. Past work on utilizing web-based entertainment information to recognize occasions [13]. NLP pipeline recognized 'helpful' tweets with accuracy 0.9256.…”

Section: Iiliterature Riviewmentioning

confidence: 99%

“…Its distinctive characteristics and extensive utilization offer an unparalleled opportunity for promptly grasping public sentiments and viewpoints. In times of emergency, Twitter functions as a crucial channel of communication for individuals to exchange updates, seek aid, and voice their apprehensions [13]. Whether it pertains to a natural catastrophe, political turmoil, or a public health crisis, users of Twitter frequently resort to the platform to furnish personal narratives and disseminate valuable data.…”

Section: Iintroductionmentioning

confidence: 99%

Twitter Trending Topics Analysis Using Power BI on Real-Time Basis

Ranjan

2024

IJSREM

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In today’s world of growing social media usage twitter has become an enormous source of data from public in form of tweets. These tweets can be collect and useful to extract effective meaningful insights ex, public sentiments, choices and opinions about a particular event, product or person, which can prove helpful for business growth, political parties and celebrities to know public choices, sentiments and their reviews over a particular product , person or government decision. But before performing sentiment analysis on tweets data we need to clean and per-process it as tweets data is highly unstructured and noisy for that many methods are there:-data per-processing, stop-words removal, stemming, lemmatization etc. After data cleaning effective meaningful insights can be extracted and sentiment analysis can be performed to extract public opinions and their choices. Twitter data possesses significant power in capturing timely public opinions on a variety of topics, such as preferences for products, political tendencies, and sentiments within the business realm. The extensive user base of this platform provides a wide array of viewpoints, rendering it a valuable resource for comprehending consumer behaviour, political developments, and market outlooks. By employing sentiment analysis, organizations can measure levels of customer contentment, policymakers can evaluate public reception, and corporations can monitor how their brands are perceived to make well-informed decisions. The real-time aspect and high level of user involvement associated with Twitter data render it priceless for shaping strategies related to product innovation, political campaigns, and business expansion efforts. This research paper highlights the power of power BI tool in data visualization of real-time tweets of a user twitter account and builds effective dashboards and also perform sentiment analysis of real-time tweets. Keywords: LDA, Machine Learning, Topic Modelling, Sentiment Analysis, NLP, Social Media.

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“…The main objective of this paper is to understand the effect of social media activities in predicting power outages. Therefore, we collected social media activities from two leading platforms, Twitter and Reddit, using weather and power-outage-related keywords such as power outage, outage, blackout, weather, storm, and more [26]. This subsection describes this data.…”

Section: ) Social Media Datamentioning

confidence: 99%

Social Media Sensors for Weather-Caused Outage Prediction Based on Spatio–Temporal Multiplex Network Representation

Aljurbua,

Alshehri,

Alharbi

et al. 2023

IEEE Access

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This study investigates severe weather events that lead to power outages. Despite extensive research on using social media during disasters, little work has focused on combining social media information with power outage data. To address this limitation, we propose a novel and effective approach to enhance the prediction accuracy of weather-related power outages by learning a spatio-temporal multiplex network that integrates information on the impact of inclement weather on the residents extracted from their social media posts with relevant weather, geographic, and grid topology data. In the multiplex network framework, the outage risk is estimated using logistic regression, neural network, support vector machine, random forest, xgboost, and decision tree classifiers through which machine learning models are applied separately on individual layers and jointly on multiplex networks representing layers capturing information related to weather, lightning, and vegetation. Experiments were conducted to predict the risk for weather-related power outages three hours in advance at a county level in five states of the Pacific Northwest region from November 2021 to April 2022. Evidence suggests that using vegetation information improves the quality of all models compared to relying on weather and lighting layers alone. Integrating an additional layer on the impact of inclement weather on the community residents retrieved from public social media posts (Twitter, Reddit) with weather, lightning, and vegetation layers improves the accuracy of outage prediction by 5% − 7%. The results demonstrate that the proposed spatio-temporal multiplex network-based approach offers beneficial insights for predicting power outages three hours ahead at the county level.INDEX TERMS Multiplex network, power outage, spatio-temporal, social media, weather, machine learning.

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#Outage: Detecting Power and Communication Outages from Social Networks

Cited by 20 publications

References 17 publications

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Twitter Trending Topics Analysis Using Power BI on Real-Time Basis

Social Media Sensors for Weather-Caused Outage Prediction Based on Spatio–Temporal Multiplex Network Representation

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