Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Sabet, Alireza Javadian

doi:10.31237/osf.io/6m5f2

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…We plan to build the social media core proposed in [9] as a tool [69,70] to characterize urban mobility patterns [71,72]. Moreover, the social media core will enable the system's stakeholders to understand user preferences during online events [73,74] which bring many travelers to specific European cities. Consequently, we plan to design predictive models as proposed in [75] to predict the popularity of online content generated by the stakeholders to maximize the visibility and popularity of their news and advertisements.…”

Section: Discussionmentioning

confidence: 99%

THOR: A Hybrid Recommender System for the Personalized Travel Experience

Sabet

Shekari

Guan

et al. 2022

BDCC

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One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected.

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Section: Discussionmentioning

confidence: 99%

THOR: A Hybrid Recommender System for the Personalized Travel Experience

Sabet

Shekari

Guan

et al. 2022

BDCC

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“…SM users generate a massive amount of accessible content. To leverage benefits from the SM data as a key to crucial insights into human behavior, many studies have been conducted to perform analysis of SM data by scholars, corporations, politicians, journalists, and governments [28][29][30][31][32][33][34][35].…”

Section: Social Mediamentioning

confidence: 99%

Graph-Based Conversation Analysis in Social Media

Brambilla

Sabet

Kharmale

et al. 2022

BDCC

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Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users’ intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities.

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“…Studying these events provides useful insights for the brands for future decisionmaking purposes. Motivated by the LRLE's potential as discussed in various works [45][46][47][48][49][50][51], we chose Milano Fashion Week, which is one of the Big Four fashion capitals.…”

Section: Events Selectionmentioning

confidence: 99%

Analyzing Brand Awareness Strategies on Social Media in the Luxury Market: the Case of Italian Fashion

Brambilla¹,

Badrizadeh²,

Mohammadi³

et al. 2022

Preprint

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The rapid penetration of social media has been redefining every facet of the old marketing and customer engagement tactics, not only for the low-end and mass products but also for luxury brands. In this context, brands are dealing with the challenge of keeping the balance between using mass marketing strategies concurrent with accentuating the exclusivity of their offerings. Social media can be considered a boon if brands employ them to reach the right audience and use the right platform by incorporating the right content. In this work, we propose a sector-specific, integrated, and holistic investigation of the social media strategies of luxury brands, together with the impact they generate in terms of the engagement level of the users as an indicator of their success. We provide empirical validation of the method in the sector of luxury fashion brands in the Italian market, providing qualitative and quantitative analysis of the content shared on social media, considering the type, timing, and modality of the sharing. We evaluate consumer-brand engagement in different contexts, including important live events in the field.

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Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Cited by 4 publications

References 35 publications

THOR: A Hybrid Recommender System for the Personalized Travel Experience

THOR: A Hybrid Recommender System for the Personalized Travel Experience

Graph-Based Conversation Analysis in Social Media

Analyzing Brand Awareness Strategies on Social Media in the Luxury Market: the Case of Italian Fashion

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