A Methodology to Detect and Track Breaking News on Twitter

Shukla, Anmol; Aggarwal, Dhruv; Keskar, Ravindra B.

doi:10.1145/2998476.2998491

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In the pre-processing stage, removal of hashtag, stop-word, URL and special characters and stemming were done, but there was no treatment of SAB terms. Authors in [42] proposed a model for detecting and tracking [18] proposed a framework for detecting news events from the Twitter stream in real-time. The approach used ANN to classify news relevant tweets from the stream based on AvgW2V and Mini-batch cluster to group detected tweets into events.…”

Section: Semi-supervised Learning For Event Detection In Social Media...mentioning

confidence: 99%

Real-time event detection in social media streams through semantic analysis of noisy terms

2022

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Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarization of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enrichment layer of SMAFED. We found that SMAFED outperformed other pre-processing frameworks with a lower loss function of 0.15 on the first dataset and 0.05 on the second dataset. Second, we determined the accuracy of SMAFED at detecting events from social media streams. The result of this second experiment showed that SMAFED outperformed existing event detection approaches with better Precision (0.922), Recall (0.793), and F-Measure (0.853) metric scores. The findings of the study present SMAFED as a more efficient approach to event detection in social media.

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Section: Semi-supervised Learning For Event Detection In Social Media...mentioning

confidence: 99%

Real-time event detection in social media streams through semantic analysis of noisy terms

2022

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“…In the pre-processing stage, removal of hashtag, stop-word, URL and special characters and stemming were done, but there was no treatment of SAB terms. Authors in [40] proposed a model for detecting and tracking breaking news from Twitter in real-time by employing Multinomial Naïve Bayes Classifier and DBSCAN algorithms. The proposed model could not dynamically learn from the available new sources.…”

Section: 2mentioning

confidence: 99%

SMAFED: Real-Time Event Detection in Social Media Streams

Kolajo

Daramola

Adebiyi

2021

Preprint

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Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarisation of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enrichment layer of SMAFED. We found that SMAFED outperformed other pre-processing frameworks with a lower loss function of 0.15 on the first dataset and 0.05 on the second dataset. Secondly, we determined the accuracy of SMAFED at detecting events from social media streams. The result of this second experiment showed that SMAFED outperformed existing event detection approaches with better Precision (0.922), Recall (0.793), and F-Measure (0.853) metric scores. The findings of the study present SMAFED as a more efficient approach to event detection in social media.

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A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism Services

et al. 2022

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Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning.

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A Methodology to Detect and Track Breaking News on Twitter

Cited by 3 publications

References 5 publications

Real-time event detection in social media streams through semantic analysis of noisy terms

Real-time event detection in social media streams through semantic analysis of noisy terms

SMAFED: Real-Time Event Detection in Social Media Streams

A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism Services

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