Detecting Tie Strength from Social Media Data in a Conference Setting
Soto Blázquez, Ana María (2019)
2019
Tuotantotalouden DI-ohjelma (Degree Programme in Industrial Engineering and Management)
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2019-06-24
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-201906202161
https://urn.fi/URN:NBN:fi:tuni-201906202161
Tiivistelmä
The concept of tie strength was introduced by Granovetter as “a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie”. Since the publication of this seminal study, several studies have been conducted incorporating the concept of tie strength in numerous fields.
The growing rise of social media in recent years has shaped a new way of establishing and maintaining ties between people. As a result, studies have been conducted that, based on social media data, are focused on the evaluation of tie strength between users. Social media has also positioned itself as a key tool in the development of events such as conferences, as it is consolidated as the communication platform through which to disseminate information and knowledge and networking.
Therefore, in the present study, it is sought to evaluate tie strength using publicly available Twitter data in the context of a conference. Specifically, the aim is to analyse the potential of implicit networks (particularly, mentions networks) generated in social media sites (particularly, Twitter) when evaluating tie strength and social ties, with special emphasis on weak ties and latent ties. Ultimately, the aim is to obtain conclusions that result in the demonstration of the utility and the advantages of implementing this analysis in the recommendation systems in conferences.
To address the main statement problem, this study starts with a review of the existing literature related to the topic. Subsequently, as regards the empirical part of the study, a case study approach is conducted. Specifically, a longitudinal single-case analysis is analysed, since the mentions networks generated from the publicly available Twitter data of the conference HICSS along nine editions (from 2010 to 2018) are studied. Different measures of social network analysis have been used to obtain results and conclusions.
Based on the analysis, different potentially useful measures for the evaluation of mentions networks and social ties are identified. These measures have served to analyse the social structures formed in a conference setting (highlighting star structures that reflect the information disseminating role of certain nodes), to identify the most relevant and influential participants (which generally correspond to important roles of the conference, as organizers or speakers), or to observe tendencies and groupings in communities according to common interests, among others.
The growing rise of social media in recent years has shaped a new way of establishing and maintaining ties between people. As a result, studies have been conducted that, based on social media data, are focused on the evaluation of tie strength between users. Social media has also positioned itself as a key tool in the development of events such as conferences, as it is consolidated as the communication platform through which to disseminate information and knowledge and networking.
Therefore, in the present study, it is sought to evaluate tie strength using publicly available Twitter data in the context of a conference. Specifically, the aim is to analyse the potential of implicit networks (particularly, mentions networks) generated in social media sites (particularly, Twitter) when evaluating tie strength and social ties, with special emphasis on weak ties and latent ties. Ultimately, the aim is to obtain conclusions that result in the demonstration of the utility and the advantages of implementing this analysis in the recommendation systems in conferences.
To address the main statement problem, this study starts with a review of the existing literature related to the topic. Subsequently, as regards the empirical part of the study, a case study approach is conducted. Specifically, a longitudinal single-case analysis is analysed, since the mentions networks generated from the publicly available Twitter data of the conference HICSS along nine editions (from 2010 to 2018) are studied. Different measures of social network analysis have been used to obtain results and conclusions.
Based on the analysis, different potentially useful measures for the evaluation of mentions networks and social ties are identified. These measures have served to analyse the social structures formed in a conference setting (highlighting star structures that reflect the information disseminating role of certain nodes), to identify the most relevant and influential participants (which generally correspond to important roles of the conference, as organizers or speakers), or to observe tendencies and groupings in communities according to common interests, among others.