Mining micro-influencers from social media posts

Micro-influencers have triggered the interest of commercial brands, public administrations, and other stakeholders because of their demonstrated capability of sensitizing people within their close reach. However, due to their lower visibility in social media platforms, they are challenging to be identified. This work proposes an approach to automatically detect micro-influencers and to highlight their personality traits and community values by computationally analyzing their writings. We introduce two learning methods to retrieve Five Factor Model and Basic Human Values scores. These scores are then used as feature vectors of a Support Vector Machines classifier. We define a set of rules to create a micro-influencer gold standard dataset of more than two million tweets and we compare our approach with three baseline classifiers. The experimental results favor recall meaning that the approach is inclusive in the identification

Elite Tweets: Analysing the Twitter Communication Patterns of Labour Party Peers in the House of Lords

The micro-blogging platform Twitter has gained notoriety for its status as both a communication channel between private individuals, and as a public forum monitored by journalists, the public, and the state. Its potential application for political communication has not gone unnoticed; politicians have used Twitter to attract voters, interact with constituencies and advance issue-based campaigns. This article reports on the preliminary results of the research team’s work with 21 peers sitting on the Labour frontbench. It is based on the monitoring and archival of the peers’ activity on Twitter for a period of 100 days from 16th May to 28th September 2012. Using a sample of more than 4,363 tweets and a mixed methodology combining semantic analysis, social network analysis and quantitative analysis, this paper explores the peers’ patterns of usage and communication on Twitter. Key findings are that as a tweeting community their behavior is consistent with others, however there is evidence that a coherent strategy is lacking. Labour peers tend to work in ego networks of self-interest as opposed to working together to promote party polic

Biased behavior in web activities: from understanding to unbiased visual exploration

Las tendencias actuales en la Web apuntan hacia la personalización de contenido, lo que no sería un problema en un mundo uniforme y sin sesgos, pero nuestro mundo no es ni uniforme ni libre de sesgos. En esta tesis planteamos la hipótesis de que los sesgos sistémicos y cognitivos que afectan a las personas en el mundo físico también afectan el comportamiento de éstas al explorar contenido en la Web. Proponemos que es posible fomentar una disminución en el comportamiento sesgado a través de una mirada holística que incluye cuantificación de sesgos, formulación de algoritmos, y diseño de interfaces de usuario. Estas tres partes del proceso propuesto son implementadas utilizando técnicas de Minería de la Web. A su vez, son guiadas por las Ciencias Sociales, y presentadas a través de sistemas Casuales de Visualización de Información. Seguimos un enfoque transversal en el cual se aplica este proceso con diferentes niveles de profundidad a lo largo de tres casos de estudio en Wikipedia y Twitter. Como resultado, observamos que los sesgos presentes en el mundo físico efectivamente se ven reflejados en plataformas Web, afectando el contenido, la percepción y el comportamiento de las personas. A través del análisis transversal de los casos de estudio, se presentan las siguientes conclusiones: 1) las herramientas de Minería de la Web son efectivas para medir y detectar comportamiento sesgado; 2) las técnicas de Visualización de Información enfocadas en personas no expertas fomentan el comportamiento no sesgado; y 3) no existen soluciones universales, y en adición a los contextos sociales y culturales, los sesgos deben ser considerados a la hora de diseñar sistemas. Para alcanzar estas conclusiones se implementaron sistemas "en la selva", evaluados de manera cuantitativa en un entorno no controlado, con un enfoque en métricas de participación y compromiso. El uso de dichas métricas es una contribución de la tesis, ya que probaron ser efectivas al medir diferencias en el comportamiento en sistemas exploratorios

What Scale of Audience a Campaign can Reach in What Price on Twitter?

Abstract—Campaigns with commercial and spam purposes have flooded the Twitter community. To understand what scale of audience a campaign could reach, we first perform a measurement study by collecting a dataset of about 10 million tweets via streaming API and one million search tweets for targeting topics, as well as 37,313 user accounts that are suspended by Twitter. From the dataset, we extract a spam campaign and a commercial promotion campaign accompanied by spamming activities. Then, we characterize the way in which a campaign can reach its audience, especially revealing the features that dominate the information diffusion. After identifying the accounts suspended by Twitter, we further inspect to what extent these features can help to weed out spam accounts. Also, the retrospective inspection is useful to uncover the tactics that malicious accounts utilize to avoid being suspended. Using the measurement results, we then develop a theoretical framework based on an epidemic model to investigate the dynamics of spammers and victims whom spammers reach in the spam campaign. With the theoretical framework, we conduct a benefit-cost analysis of the spam campaign, shedding lights on how to restrict the benefit of the spam campaign. I