Fashioning Data - A Social Media Perspective on Fast Fashion Brands

Chakraborty, Rupak; Kundu, Senjuti; Agarwal, Prakul

doi:10.18653/v1/w16-0407

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…These platforms enable users to generate and consume content simultaneously on a daily basis leading to various forms of information, making social media a rich source of longitudinal data from diverse users. Because users decide what to share and engage openly or privately, a huge amount of resourceful and self-policing data is available [1]. Concerning migrants and refugees, the recognition of a changing environment and of citizen's opinions play a pivotal role in ensuring effective regulation.…”

Section: Social Networkmentioning

confidence: 99%

“…Sentiment analysis deals with understanding the polarity of opinion, i.e. positive, neutral or negative, and sometimes the context of expression [1]. Ljubešić and Fišer [27] utilised tweets annotated with location information and emoticons to study the mood of users at different geographical locations.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Modern social media platforms such as Twitter 1 and Facebook 2 are very popular with the public for many reasons. Users of the platforms can generate and consume content simultaneously on a daily basis leading to various forms of information, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Users share information about virtually all aspects of their social life making social media platforms ideal for studying various aspects of social phenomena 3 . These capabilities lead to vast amounts of resourceful data from diverse users [1]. The platforms offer crucial avenues where users express views on global issues and can affect, and be useful in understanding how modern society functions [2].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Migration and Refugee Crisis: a Critical Analysis of Online Public Perception

Inuwa-Dutse,

Liptrott,

Korkontzelos

2020

Preprint

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The migration rate and the level of resentments towards migrants are an important issue in modern civilisation. The infamous EU refugee crisis caught many countries unprepared, leading to sporadic and rudimentary containment measures that, in turn, led to significant public discourse. Decades of offline data collected via traditional survey methods have been utilised earlier to understand public opinion to foster peaceful coexistence. Capturing and understanding online public opinion via social media is crucial towards a joint strategic regulation spanning safety, rights of migrants and cordial integration for economic prosperity. We present a analysis of opinions on migrants and refugees expressed by the users of a very popular social platform, Twitter. We analyse sentiment and the associated context of expressions in a vast collection of tweets related to the EU refugee crisis. Our study reveals a marginally higher proportion of negative sentiments vis-á-vis migrants and a large proportion of the negative sentiments is more reflected among the ordinary users. Users with many followers and non-governmental organisations (NGO) tend to tweet favourably about the topic, offsetting the distribution of negative sentiment. We opine that they can be encouraged to be more proactive in neutralising negative attitudes that may arise concerning similar incidences.

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Section: Social Networkmentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Migration and Refugee Crisis: a Critical Analysis of Online Public Perception

Inuwa-Dutse,

Liptrott,

Korkontzelos

2020

Preprint

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FITNet

Han

Chen

Jin

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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The rise of social media has changed the nature of the fashion industry. Influence is no longer concentrated in the hands of an elite few: social networks have distributed power across a broader set of tastemakers. To understand this new landscape of influence, we created FITNet --- a network of the top 10k influencers of the larger Twitter fashion graph. To construct FITNet, we trained a content-based classifier to identify fashion-relevant Twitter accounts. Leveraging this classifier, we estimated the size of Twitter's fashion subgraph, snowball sampled more than 300k fashion-related accounts based on following relationships, and identified the top 10k influencers in the resulting subgraph. We use FITNet to perform a large-scale analysis of fashion influencers, and demonstrate how the network facilitates discovery, surfacing influencers relevant to specific fashion topics that may be of interest to brands, retailers, and media companies.

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A Methodology for Brand Name Hierarchical Clustering Based on Social Media Data

Shah¹,

Naveed²,

Rauf³

2018

Journal of Applied and Emerging Sciences

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Clustering of brand names is a common technique for identification of different groups of brand names with maximum effectiveness and similarities. In this study, we proposed a methodology for brand name clustering, based on data from social media like Twitters. Dataset consist of tweets mentioning various garment brands in them. In order to cluster the brand names, we have proposed an algorithm, named BNACA (Brand Names Agglomerate Clustering Algorithm), an extension to the standard hierarchical clustering algorithm. In the proposed algorithm, we used single linkage as a similarity measure. The proposed clustering algorithm provides consistent clustered results for various sets of brand names of garment industry. Finally, clusters of garment brand names are visualized through dendogram. The dendogram clearly shows the maximum similarities among garment brand names.

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Fashioning Data - A Social Media Perspective on Fast Fashion Brands

Cited by 3 publications

References 10 publications

Migration and Refugee Crisis: a Critical Analysis of Online Public Perception

Migration and Refugee Crisis: a Critical Analysis of Online Public Perception

FITNet

A Methodology for Brand Name Hierarchical Clustering Based on Social Media Data

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