Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14

A million tweets are worth a few points : tuning transformers for customer service tasks

In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions. We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings

Social media as a data gathering tool for international business qualitative research: opportunities and challenges

Lusophone African (LA) multinational enterprises (MNEs) are becoming a significant pan-African and global economic force regarding their international presence and influence. However, given the extreme poverty and lack of development in their home markets, many LA enterprises seeking to internationalize lack resources and legitimacy in international markets. Compared to higher income emerging markets, Lusophone enterprises in Africa face more significant challenges in their internationalization efforts. Concomitantly, conducting significant international business (IB) research in these markets to understand these MNEs internationalization strategies can be a very daunting task. The fast-growing rise of social media on the Internet, however, provides an opportunity for IB researchers to examine new phenomena in these markets in innovative ways. Unfortunately, for various reasons, qualitative researchers in IB have not fully embraced this opportunity. This article studies the use of social media in qualitative research in the field of IB. It offers an illustrative case based on qualitative research on internationalization modes of LAMNEs conducted by the authors in Angola and Mozambique using social media to identify and qualify the population sample, as well as interact with subjects and collect data. It discusses some of the challenges of using social media in those regions of Africa and suggests how scholars can design their studies to capitalize on social media and corresponding data as a tool for qualitative research. This article underscores the potential opportunities and challenges inherent in the use of social media in IB-oriented qualitative research, providing recommendations on how qualitative IB researchers can design their studies to capitalize on data generated by social media.https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406Accepted manuscriptPublished versio

Clustering Memes in Social Media

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'13), 201

Collaborative pedagogy and digital scholarship: a case study of 'Media Culture 2020'

This paper presents an educational case study of ‘Media Culture 2020’, an EU Erasmus Intensive Programme that utilised a range social media platforms and computer software to create open, virtual spaces where students from different countries and fields could explore and learn together. The multi-disciplinary project featured five universities from across Europe and was designed to develop new pedagogical frameworks to encourage collaborative approaches to teaching and learning in the arts. The main objective of the project was to break down classroom and campus walls by creating digital learning environments that facilitated new forms of production, transmission and representation of knowledge. Media Culture 2020 was designed to pilot a novel mode of ‘blended learning’, demonstrating a number of ways in which ‘Web 2.0’ networked technologies might be adopted by academics to encourage open and collaborative modes of practice. The project utilised a number of social media platforms (including Facebook, Twitter, Google+, Google Hangout, Google Docs and Blogger) to enhance the learning experiences of a diverse set of students from different cultural and international contexts. In doing so, Media Culture 2020 enabled participants with a diverse range skills and cultural experiences to develop new working practices that respond to the convergence of digital media and art, as well as the internationalisation of media production and business, through the use of open, interactive software

A framework to extract biomedical knowledge from gluten-related tweets: the case of dietary concerns in digital era

Journal pre proofBig data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from more than 400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry-LAQV, which is financed by national funds from and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020] units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the European Regional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte, the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of [ED431C2018/55GRC] Competitive Reference Group. The authors also acknowledge the post-doctoral fellowship [ED481B2019032] of Martin PerezPerez, funded by the Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUGinfo:eu-repo/semantics/publishedVersio