Social Media Marketing como herramienta del mix de comunicación

[ES]El presente TFG plantea un análisis de la utilización de las redes sociales dentro de la estrategia de comunicación de las organizaciones empresariales. Para ello, se estudia el contexto comunicacional actual, caracterizado por la fragmentación y dispersión de las audiencias, la saturación, la pérdida de la eficacia de la publicidad tradicional, los nuevos hábitos de consumo de medios y la explosión de la conectividad, y el papel de las distintas herramientas de comunicación en entornos digitales: página web, blog corporativo, email marketing, gestión multimedia, mobile marketing, E-commerce y Social Media Marketing. En este contexto y por su relevancia, nos centramos en el estudio de las estrategias de Social Media Marketing. Para ello abordamos las ventajas e inconvenientes de la utilización de redes sociales, cómo efectuar la planificación de la comunicación en redes sociales (objetivos, público, contenidos, plataformas, plan de acción e indicadores), y las nuevas profesiones ligadas a su gestión. Un aspecto relevante que también se analiza es la gestión de la reputación online y las implicaciones que el uso de redes sociales tiene sobre ella, así como los protocolos de actuación ante posibles crisis derivadas de la presencia en estos canales. En las redes sociales se encuentran prácticamente todos los stakeholders de las empresas/marcas, fuente de información continua para mejorar sus negocios. Su utilización, dentro de una comunicación integral de marketing, permite fortalecer la imagen corporativa y lograr un posicionamiento claro y a largo plazo.

Plan de Social Media Marketing en Euskaltel

El presente estudio es un Plan de Social Media Marketing realizado para la empresa Euskaltel, compañía telefónica líder en el País Vasco. Este plan está elaborado para que se ponga en funcionamiento a partir de Septiembre del 2015. El Plan de Social Media Marketing se concreta en una serie de aspectos que se deben tener en cuenta para la puesta en marcha de una estrategia de integración de redes sociales y acciones de marketing digital por parte de la empresa. Una de las principales características del Plan de Social Media Marketing en comparación con otros planes empresariales es su flexibilidad y su disposición para ser rediseñado incluso al día siguiente de ser implantado

Using Social Media Content for Screening in Recruitment and Selection: Pros and Cons.

The article considers the arguments that have been made in defence of social media screening as well as issues that arise and may effectively erode the reliability and utility of such data for employers. First, the authors consider existing legal frameworks and guidelines that exist in the UK and the USA, as well as the subsequent ethical concerns that arise when employers access and use social networking content for employment purposes. Second, several arguments in favour of the use of social networking content are made, each of which is considered from several angles, including concerns about impression management, bias and discrimination, data protection and security. Ultimately, the current state of knowledge does not provide a definite answer as to whether information from social networks is helpful in recruitment and selection.

Adaptive Routing of QoS-constrained Media Streams over Scalable Overlay Topologies

Current research on Internet-based distributed systems emphasizes the scalability of overlay topologies for efficient search and retrieval of data items, as well as routing amongst peers. However, most existing approaches fail to address the transport of data across these logical networks in accordance with quality of service (QoS) constraints. Consequently, this paper investigates the use of scalable overlay topologies for routing real-time media streams between publishers and potentially many thousands of subscribers. Specifically, we analyze the costs of using k-ary n-cubes for QoS-constrained routing. Given a number of nodes in a distributed system, we calculate the optimal k-ary n-cube structure for minimizing the average distance between any pair of nodes. Using this structure, we describe a greedy algorithm that selects paths between nodes in accordance with the real-time delays along physical links. We show this method improves the routing latencies by as much as 67%, compared to approaches that do not consider physical link costs. We are in the process of developing a method for adaptive node placement in the overlay topology, based upon the locations of publishers, subscribers, physical link costs and per-subscriber QoS constraints. One such method for repositioning nodes in logical space is discussed, to improve the likelihood of meeting service requirements on data routed between publishers and subscribers. Future work will evaluate the benefits of such techniques more thoroughly.

Mapping the news on (social)-media. Methodological considerations for the analysis of local news networks.

info:eu-repo/semantics/nonPublished

Self-regulated learning and social media – a ‘natural’ alliance? Evidence on students’ self-regulation of learning, social media use and student-teacher relationship

Article is available at: http://www.tandfonline.com/doi/full/10.1080/17439884.2015.1064953.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Young People, Peer-to-Peer Grooming and Sexual Offending: Understanding and Responding to Harmful Sexual Behaviour within a Social Media Society

There is ample evidence that young people engage in grooming and harmful sexual behaviour (HSB) using social media with enough frequency to make those behaviours important con-cerns for both society and care providers. This article provides a critical overview of the con-ceptual and empirical foundations of peer-to-peer grooming and the use of social media with-in the context of HSB. Based on this learning, it ultimately introduces a new model of inter-vention and of professional practice, which provides the standards for micro-level decision-making about goals, methods and assessment tailored to this specific offending context.

Improved lexicon-based sentiment analysis for social media analytics

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.