Understanding social media beyond text: a reliable practice on Twitter

AbstractSocial media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information is not only included in the short textual content but also embedded in the images and videos. In this paper, we introduce an effective and efficient framework for event detection with social media data. The framework integrates both textual and imagery content in the hope to fully utilize the information. The approach has been demonstrated to be more accurate than the text-only approach by removing 58 (66.7%) false-positive events. The precision of event detection is improved by 6.5%. Besides, based on our analysis, we also look into the content of these images to further explore the space of social media studies. Finally, the closely related text and image from social media offer us a valuable text-image mapping, which can enable knowledge transfer between two media types.

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Understanding Social Media Beyond Text: A Reliable Practice on Twitter

10.21203/rs.3.rs-24618/v2 ◽

2020 ◽

Author(s):

Qixuan Hou ◽

Meng Han ◽

Feiyang Qu ◽

Jing (Selena) He

Keyword(s):

Social Media ◽

Event Detection ◽

High Volume ◽

Time Data ◽

Positive Events ◽

Image Mapping ◽

Related Text ◽

Rich Information ◽

The Rich ◽

Textual Content

Abstract Social media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information is not only included in the short textual content but also embedded in the images and videos. In this paper, we introduce an effective and efficient framework for event detection with social media data. The framework integrates both textual and imagery content in the hope to fully utilize the information. The approach has been demonstrated to be more accurate than the text-only approach by removing 58 (66.7%) false-positive events. The precision of event detection is improved by 6.5%. Besides, based on our analysis, we also look into the content of these images to further explore the space of social media studies. Finally, the closely related text and image from social media offer us valuable text-image mapping, which can enable knowledge transfer between two media types.

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Understanding Social Media Beyond Text: A Reliable Practice on Twitter

10.21203/rs.3.rs-24618/v1 ◽

2020 ◽

Author(s):

Qixuan Hou ◽

Meng Han ◽

Feiyang Qu

Keyword(s):

Social Media ◽

Event Detection ◽

Disaster Response ◽

High Volume ◽

Fine Tuning ◽

Time Data ◽

Short Text ◽

The Rich ◽

Media Imagery ◽

Text Content

Abstract Social media has been broadly applied in many applications in sales, marketing, event detection, etc. With high-volume and real-time data, social media has also been used for disaster responses. However, distinguishing between rumors and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. Furthermore, the rich information is not only included in the short text content but also embedded in the images, videos. In this paper, to address the emerging challenge of disaster response, we introduce a reliable framework for disaster information understanding and response with a practice on Twitter. The framework integrates both textual and imagery content from tweets in hope to fully utilize the information. The text classifier is built to remove noises, which can achieve 0.92 F1-score in classifying individual tweet. The image classifier is constructed by fine-tuning pre-trained VGG-F network, which can achieve 90\% accuracy. The image classifier serves as a verifier in the pipeline to reject or confirm the detected events. The evaluation indicates that the verifier can significantly reduce false positive events. We also explore Twitter-based drought management system and infrastructure monitoring system to further study the impacts of imagery content on event detection systems and we are able to pinpoint additional benefits which can be gained from social media imagery content.

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Embed2Detect: temporally clustered embedded words for event detection in social media

Machine Learning ◽

10.1007/s10994-021-05988-7 ◽

2021 ◽

Author(s):

Hansi Hettiarachchi ◽

Mariam Adedoyin-Olowe ◽

Jagdev Bhogal ◽

Mohamed Medhat Gaber

Keyword(s):

Social Media ◽

Event Detection ◽

High Volume ◽

Detection Methods ◽

Word Embeddings ◽

Agglomerative Clustering ◽

Data Set ◽

Social Media Data ◽

Social Media Platforms ◽

Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

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Enhanced Event Detection in Twitter Through Feature Analysis

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2019070101 ◽

2019 ◽

Vol 14 (3) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Dharini Ramachandran. ◽

Parvathi R.

Keyword(s):

Social Media ◽

Event Detection ◽

Situational Awareness ◽

Crisis Response ◽

Rescue Operation ◽

Digital Era ◽

Digital Document ◽

Rich Information ◽

Improved Performance ◽

The Many

The Digital era has the benefits in unearthing a large amount of imperative material. One such digital document is social media data, which when processed can give rise to information which can be helpful to our society. One of the many things that we can unearth from social media is events. Twitter is a very popular microblog that encompasses fruitful and rich information on real world events and popular topics. Event detection in view of situational awareness for crisis response is an important need of the current world. The identification of tweets comprising information that may assist in help and rescue operation is crucial. Most pertinent features for this process of identification are studied and the inferences are given in this article. The efficiency and practicality of the features are discussed here. This article also presents the results of experimentation carried out to assess the most relevant combination of features for improved performance in event detection from Twitter.

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A critical appraisal of diversity in digital knowledge production: Segregated inclusion on YouTube

New Media & Society ◽

10.1177/14614448211034846 ◽

2021 ◽

pp. 146144482110348

Author(s):

Kaiping Chen ◽

June Jeon ◽

Yanxi Zhou

Keyword(s):

Social Media ◽

Knowledge Production ◽

Science Communication ◽

Critical Appraisal ◽

Digital Platforms ◽

The Core ◽

Inclusion Phenomenon ◽

Media Platform ◽

The Rich ◽

Digital Knowledge

Diversity in knowledge production is a core challenge facing science communication. Despite extensive works showing how diversity has been undermined in science communication, little is known about to what extent social media augments or hinders diversity for science communication. This article addresses this gap by examining the profile and network diversities of knowledge producers on a popular social media platform—YouTube. We revealed the pattern of the juxtaposition of inclusiveness and segregation in this digital platform, which we define as “segregated inclusion.” We found that diverse profiles are presented in digital knowledge production. However, the network among these knowledge producers reveals the rich-get-richer effect. At the intersection of profile and network diversities, we found a decrease in the overall profile diversity when we moved toward the center of the core producers. This segregated inclusion phenomenon questions how inequalities in science communication are replicated and amplified in relation to digital platforms.

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Real-time spatio-temporal event detection on geotagged social media

Journal Of Big Data ◽

10.1186/s40537-021-00482-2 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yasmeen George ◽

Shanika Karunasekera ◽

Aaron Harwood ◽

Kwan Hui Lim

Keyword(s):

New York ◽

Social Media ◽

Event Detection ◽

Detection System ◽

Time And Space ◽

Social Media Data ◽

Event Time ◽

Spatio Temporal ◽

Geographical Space ◽

Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

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