Exploring the Long Tail of Social Media Tags

Kordumova, Svetlana; Gemert, Jan C. van; Snoek, Cees G. M.

doi:10.1007/978-3-319-27671-7_5

Cited by 9 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There were only 10 hashtags with a degree higher than 500. On the other hand, most hashtags had a degree under 100, which is a strong indication of the standard behavior in social networks regarding hashtag usage (Kordumova et al, 2016). Degree distribution is shown in Fig.2 and Table 5.…”

Section: Cluster Analysismentioning

confidence: 92%

See 1 more Smart Citation

Customer Experience with Organic Food: Global View

2018

View full text Add to dashboard Cite

In recent years, organic food production has been rising dramatically both in the EU and the USA. Previous research on consumer perception of organic food has mainly employed questionnaire survey methods. However, in the current age of the social network phenomenon, social media could prove to be a rich source of data. Increasingly, consumers are using social networks to share personal attitudes and experiences. This shared content could inform us about consumer opinions. Social network analysis and related sentiment analysis could allow identification of consumers’ experience and feelings about organic food. We investigated the perception of organic food using 1,325,435 Instagram interactions by 313,883 users worldwide. The data were recorded between July 4, 2016, and April 19, 2017. We identified three major hashtag areas (healthy, vegan, and clean food). The sentiment analysis revealed three dominant areas related to the #organicfood hashtag (feelings, taste, and appearance). Cluster analysis extracted four areas, as follows: Healthy living, Vegetarian, vegan, and raw diets, Clean eating, and Active healthy living. The mentioned communities are significant and useful at identification of customers values for farmers organic food product management and marketing communication in terms of product positioning.

show abstract

Section: Cluster Analysismentioning

confidence: 92%

“…Degree distribution corresponds with the long tail attribute (Kordumova et al, 2016). There were only 10 hashtags with a degree higher than 500.…”

Section: Cluster Analysismentioning

confidence: 99%

Customer Experience with Organic Food: Global View

2018

View full text Add to dashboard Cite

show abstract

“…Figure 8 shows the frequency of 'total cheers' submitted by the cheering spectators. This results in a long tail distribution that is characteristic in contexts that involve crowd-engagement [16,26]. Figure 7 plots the change in gradient (of altitude) and the cheers submitted.…”

Section: General Observationsmentioning

confidence: 99%

Embedding a Crowd inside a Relay Baton

Curmi

Ferrario

Whittle

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

This paper presents a digital relay baton that connects longdistance runners with distributed online spectators. The baton broadcasts athletes' live locative data to a social network and communicates back remote-crowd support through haptic and audible cheers. Our work takes an exploratory design approach to bring new insights into the design of real-time techno-mediated social support. The prototype was deployed during a 170-mile charity relay race across the UK with 13 participants, 261 on-line supporters, and gathered a total of 3,153 'cheers'. We report on the insights collected during the design and deployment process and identify three fundamental design considerations: the degree of spectator expression that the design affords, the context applicability, and the data flow within the social network.

show abstract

“…All datasets are tokenized using the Stanford NLP tool (Manning et al, 2014). Since postings in social networks by both users and products follow the long tail distribution (Kordumova et al, 2016), we only show the distribution of total number of posts for different products. For example, #p(0-50) means the number of products which have reviews between the size of 0 to 50.…”

Section: Datasetsmentioning

confidence: 99%

Dual Memory Network Model for Biased Product Review Classification

Long¹,

Ma²,

Lu³

et al. 2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

View full text Add to dashboard Cite

In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-theart unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values. arXiv:1809.05807v1 [cs.CL]

show abstract

Exploring the Long Tail of Social Media Tags

Cited by 9 publications

References 24 publications

Customer Experience with Organic Food: Global View

Customer Experience with Organic Food: Global View

Embedding a Crowd inside a Relay Baton

Dual Memory Network Model for Biased Product Review Classification

Contact Info

Product

Resources

About