Active learning in annotating micro-blogs dealing with e-reputation

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science - Vol 3 - Contextualisation digitale - 201

REINA at RepLab2013 Topic Detection Task: Community Detection

Social networks have become a large repository of comments which can extract multiple information. Twitter is one of the most widespread social networks and larger and is therefore an important source for detecting states of opinion, events and happenings before even the mainstream media. Topic detection is important to discover areas of interest that arise in the tweets. We have used classical systems for a similarity matrix and we have used community detection techniques. The results have been good and allows us to study new possibilities

REINA at RepLab2013 Topic Detection Task: Community Detection

[EN]Social networks have become a large repository of comments which can extract multiple information. Twitter is one of the most widespread social networks and larger and is therefore an important source for detecting states of opinion, events and happenings before even the mainstream media. Topic detection is important to discover areas of interest that arise in the tweets. We have used classical systems for a similarity matrix and we have used community detection techniques. The results have been good and allows us to study new possibilities

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Twitters Impact on Sports Media Relations

The introduction of Social Media (SM) into sports communications in professional leagues is disrupting the traditional methods of sports media relations. In the past, teams used websites to post information for fans, but it was strictly a one-way format of communication whereby a story was posted for fans to read. To fully engage with this new communication channel, the sports communications departments in professional leagues have begun to use SM to communicate directly with fans through platforms like Twitter and Facebook. Currently, SM like Twitter allows the team communication departments to communicate directly with fans in an interactive two-way format that is not mediated by a reporter or someone from a traditional media outlet. In addition, the open format of SM means that media relations staff are no longer the only intermediary between the media and the players; through the use of SM like Twitter, a professional athlete can now communicate directly to fans without gatekeepers like the media or the sports communications department of the team. This thesis will explore how SM has changed media relations from several different perspectives. The first perspective is related to the risks that are associated with the use of SM by professional athletes: without an intermediary or a filter for athlete-fan communication, many athletes have caused irreparable damage to their reputation and the reputation of their team. The second perspective is related to the benefits for teams that use SM as a platform to connect with fans: the ability to connect with fans using SM is new to sports communications and represents an interactive one-to-one and one-to-many mode of communication through which the fan can directly communicate with the team. Finally, this research will look at how Twitter has changed media relations in sports from the perspective of the lived experiences of people who work in sports media. To explore the risks associated with athletes’ use of social media, this research used Situational Crisis Communication Theory as a theoretical framework to explore reputation-damaging incidents that occurred through social media. The study reviewed national media stories reported in North America from 2009 to 2010 that were perceived to have negative impact on athletes’ reputation. In total, 17 incidents were reviewed — seven incidents in particular demonstrated the athlete as the source of the SM crisis. Through the review and categorization of these 17 situations, the study was able to identify four broad categories of situations that a sports communication manager needs to be prepared for. The four categories identified were “Rookie Reporter”, “Team Insider”, “Opportunist”, and “Imposter”. Each of these categories are invaluable for team communication managers to recognize in order to address the risks associated with social media. To explore the benefits associated with the communications department’s use of social media, this research used Uses and Gratification theory as a theoretical framework to explore how and why fans followed team Twitter accounts. This study was conducted in partnership with the Canadian Football League (CFL) and a total of 526 people responded to an online survey that was tweeted out to them for their feedback. The results of the survey indicated several significant findings — in particular, the phenomenon of converged sports fan consumption was identified, which has not been previously acknowledged in academic research. The phenomenon of converged sports fan refers to the multi-screen environment whereby a sports fan decides where, when, and how they want to consume sporting content. This research identified that in-game consumption of SM while watching television and the mobile consumption of SM are both dominant ways for fans to interact with their teams. This multi-modal format of connecting with the team supports the idea of Henry Jenkins’s Black Box Fallacy (2006, p. 13): as teams move forward in developing communications platforms to reach their fans, they will need to recognize that all channels can and do work together. In order to further understand how Twitter has changed sports media relations, the study used long semi-structured interviews with a phenomenological research design to understand how Twitter has impacted sports media relations. The phenomenological analysis of the informant interviews suggested that Twitter is the source of three themes of change: general media relations, mechanical job functions, and other changes specific to sports media relations. The significance of Twitter’s impact on sports media relations cannot be understated. With the ubiquitous use of SM like Twitter, it is important to understand how sports media relations can use SM to manage the image of their respective teams and athletes. After looking at SM and sports from three different perspectives, the pivotal finding was the role that Twitter and mobile communications play in ‘flattening’ sports media relations. Similar to how Friedman (2006) argued that the convergence of the personal computer drove globalization, Twitter and the increased adoption of mobile communications have flattened the role of sports media relations. This research will explain how the flattening of sports media relations happened and what the implications might be for sports media professionals