The Semantic Network Model of Creativity: Analysis of Online Social Media Data

AbstractThis study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.

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Crisis-DIAS: Towards Multimodal Damage Analysis - Deployment, Challenges and Assessment

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5369 ◽

2020 ◽

Vol 34 (01) ◽

pp. 346-353 ◽

Cited By ~ 3

Author(s):

Mansi Agarwal ◽

Maitree Leekha ◽

Ramit Sawhney ◽

Rajiv Ratn Shah

Keyword(s):

Social Media ◽

Damage Assessment ◽

Damage Identification ◽

Detection System ◽

Learning Performance ◽

Social Media Data ◽

Online Social Media ◽

Assessment Performance ◽

The Impact ◽

Media Data

In times of a disaster, the information available on social media can be useful for several humanitarian tasks as disseminating messages on social media is quick and easily accessible. Disaster damage assessment is inherently multi-modal, yet most existing work on damage identification has focused solely on building generic classification models that rely exclusively on text or image analysis of online social media sessions (e.g., posts). Despite their empirical success, these efforts ignore the multi-modal information manifested in social media data. Conventionally, when information from various modalities is presented together, it often exhibits complementary insights about the application domain and facilitates better learning performance. In this work, we present Crisis-DIAS, a multi-modal sequential damage identification, and severity detection system. We aim to support disaster management and aid in planning by analyzing and exploiting the impact of linguistic cues on a unimodal visual system. Through extensive qualitative, quantitative and theoretical analysis on a real-world multi-modal social media dataset, we show that the Crisis-DIAS framework is superior to the state-of-the-art damage assessment models in terms of bias, responsiveness, computational efficiency, and assessment performance.

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A Novel Transit Rider Satisfaction Metric: Rider Sentiments Measured from Online Social Media Data

Journal of Public Transportation ◽

10.5038/2375-0901.16.2.2 ◽

2013 ◽

Vol 16 (2) ◽

pp. 21-45 ◽

Cited By ~ 72

Author(s):

Craig Collins ◽

Samiul Hasan ◽

Satish Ukkusuri

Keyword(s):

Social Media ◽

Social Media Data ◽

Online Social Media ◽

Media Data

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Systematic Review on the Suspicious Profiles Detection on Online Social Media Data

Oriental journal of computer science and technology ◽

10.13005/ojcst/10.03.13 ◽

2017 ◽

Vol 10 (3) ◽

pp. 644-652

Author(s):

Asha Asha ◽

Dr. Balkishan

Keyword(s):

Systematic Review ◽

Data Mining ◽

Social Media ◽

Law Enforcement ◽

Social Media Data ◽

Data Mining Techniques ◽

Online Social Media ◽

Using Data ◽

Media Data ◽

Mining Tools

Escalating crimes on digital facet alarms the law enforcement bodies to keep a gaze on online activities which involve massive amount of data. This will raise a need to detect suspicious activities on online available social media data by optimizing investigations using data mining tools. This paper intends to throw some light on the data mining techniques which are designed and developed for closely examining social media data for suspicious activities and profiles in different domains. Additionally, this study will categorize the techniques under various groups highlighting their important features, challenges and application realm.

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A Framework for Identifying Influential People by Analyzing Social Media Data

Applied Sciences ◽

10.3390/app10248773 ◽

2020 ◽

Vol 10 (24) ◽

pp. 8773

Author(s):

Md. Sabbir Al Ahsan ◽

Mohammad Shamsul Arefin ◽

A. S. M. Kayes ◽

Mohammad Hammoudeh ◽

Omar Aldabbas

Keyword(s):

Social Media ◽

Apache Spark ◽

Social Media Data ◽

Online Social Media ◽

Spark Framework ◽

Media Data ◽

Day By Day ◽

Complicated Task ◽

Social Sensor ◽

New Framework

In this paper, we introduce a new framework for identifying the most influential people from social sensor networks. Selecting influential people from social networks is a complicated task as it depends on many metrics like the network of friends, followers, reactions, comments, shares, etc. (e.g., friends-of-a-friend, friends-of-a-friend-of-a-friend). Data on social media are increasing day-by-day at an enormous rate. It is also a challenge to store and process these data. Towards this goal, we use Hadoop to store data and Apache Spark for the fast computation of the data. To select influential people, we apply the mechanisms of skyline query and top-k query. To the best of our knowledge, this is the first work to apply the Apache Spark framework to identify influential people on social sensor network, such as online social media. Our proposed mechanism can find influential people very quickly and efficiently on the data pattern of Facebook.

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