Analysis of Online User Behaviour for Art and Culture Events

Rahdari, Behnam; Arabghalizi, Tahereh; Brambilla, Marco

doi:10.1007/978-3-319-66808-6_15

Cited by 6 publications

(4 citation statements)

References 13 publications

(13 reference statements)

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“…By now, the growth of SM platforms has caused massive awareness in societies across the globe. Studies [19][20][21][22][23] show how the tremendous impact of SM has penetrated the cultures and most aspects of people's lives. As studied in [24], these platforms proffer massive leverage on how social relationships and networks are established, mediated, and maintained, and consequently, the changes they bring to the society.…”

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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Section: Social Mediamentioning

confidence: 99%

Graph-Based Conversation Analysis in Social Media

Brambilla

Sabet

Kharmale

et al. 2022

BDCC

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“…Topic-based categorization is the most widely used approach in the literature, which categorizes SM events according to their topics (e.g., politics, sport, and so on [99]). According to the topic-based categorization approach, an event (i.e., a topic) might be an actual event that is held in a location and has a predefined schedule; for example, [5,87] monitor and investigate the participants' reaction to a cultural event. Also, it is possible that some events are not necessarily held physically and do not have a predefined calendar.…”

Section: Context and Motivationsmentioning

confidence: 99%

A multi-perspective approach for analyzing long-running live events on social media. A case study on the “Big Four” international fashion weeks

Sabet

Brambilla

Hosseini

2021

Online Social Networks and Media

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“…Our most recent studies [47,48] exploit Instagram and Twitter datasets from a famous art work called "The Floating Piers" that was created by the worldrenowned artists Christo and Jeanne-Claude 21 and exposed to the public view at the Lake Iseo in Italy, from June 18 through July 3, 2016. We extracted the social media content relevant to the event, during a time period from June 10 th to July 30 th 2016, that contains 30,256 Instagram posts and 14,062 tweets, using Twitter and Instagram APIs.…”

Section: The Floating Piersmentioning

confidence: 99%

Models and Practices in Urban Data Science at Scale

et al. 2019

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Cities can be observed through a broad set of sensing technologies, spanning from physical sensors in the streets, to socioeconomic reports, to other kinds of sources that are able to represent the behaviour of the citizens and visitors, such as mobile phone records, social media posts, and other digital traces. In this paper, we propose a conceptual framework for putting at use this variety of Big Data sources, with a unified approach that applies spatial and temporal analysis over heterogeneous streams of data. We define spatial analysis based on conceptual grids (made of cells) over the city space, and then we study: the time series of signals both at grid and cell level; the correlation across signals and across cells; the prediction of city dynamics based on multiple signals; and the identifications of anomalies based on the difference between the observed dynamics and their prediction. To implement this model we propose a general architectural framework that uses Big Data technologies (such as HDFS, YARN, HIVE, PIG, Cascalog, Spark, Spark SQL, Spark Streaming and SparkR) and can be deployed in different configurations based on different needs. By taking an inherent data science approach to the problem we are able to address at scale: technical problems such as heterogeneous time and space granularity of the data, as well as appropriate interpretation of the results through tools that enable intuitive and immediate visual perception of emerging patterns and dynamics. We demonstrate feasibility, generality and effectiveness of our Urban Data Science at scale approach through multiple use cases and examples taken from real-world requirements collected in various cities and accounting for diverse business and city needs.

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Analysis of Online User Behaviour for Art and Culture Events

Cited by 6 publications

References 13 publications

Graph-Based Conversation Analysis in Social Media

Graph-Based Conversation Analysis in Social Media

A multi-perspective approach for analyzing long-running live events on social media. A case study on the “Big Four” international fashion weeks

Models and Practices in Urban Data Science at Scale

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