The RepLab 2014 Dataset

RepLab 2014 used Twitter data in English and Spanish. For the reputation dimensions task, the data set is the same as in Replab 2013. This corpus consists of a collection of tweets referring to a selected set of 61 entities from four domains: automotive, banking, universities and music/artists. Replab 2014 will use only the automotive and banking subsets. Crawling was performed during the period from the 1st June 2012 to the 31st Dec 2012 using the entity’s canonical name as query. For each entity, at least 2,200 tweets were collected: at least 700 tweets at the beginning of the timeline are used as training set, and at least 1,500 last tweets are reserved for the test set. The corpus also comprises additional background tweets for each entity (up to 50,000, with a large variability across entities). Note that the final amount of available tweets in these sets may be lower, since some posts may have been deleted or made private by the authors: in order to respect Twitter’s terms of service, the organizers do not provide the contents of the tweets. The tweet identifiers can be used to retrieve the texts of the posts similarly to the mechanism used in the TREC Microblog Track in 2011 and 2012. Each tweet is categorized into one of the following reputation dimensions: Products/Services, Innovation, Workplace, Citizenship, Governance, Leadership, Performance and Undefined. For the author profiling task, the data set consists of over 8,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time. Reputation experts will manually identify the opinion makers (i.e. authors with reputational influence) and annotate them as “Influencer”. All those profiles that are not considered opinion makers will be assigned the “Non-Influencer” label. In case a profile cannot be classified into one of these categories, it will be labelled as “Undecidable”. Each opinion maker will be categorized as journalist, professional, authority, activist, investor, company, or celebrity. The data set will be split into training and test sets. The estimatated proportion is 30% and 70% respectively, although the exact splits will be given later.RepLab 2014采用英语与西班牙语的Twitter数据。 针对声誉维度任务，本次数据集与RepLab 2013完全一致。该语料库收录了覆盖四大领域的61个选定实体的相关推文，四大领域分别为汽车、银行、高等院校以及音乐/艺人领域。RepLab 2014仅使用其中的汽车与银行子数据集。数据爬取工作于2012年6月1日至2012年12月31日期间开展，爬取时以实体的规范名称作为查询词。针对每个实体，至少采集2200条推文：其中时间线初始阶段的至少700条推文用作训练集，末尾的至少1500条推文留作测试集。该语料库还包含每个实体的额外背景推文（单实体最多可达5万条，不同实体间采集数量差异显著）。需注意，由于部分推文可能已被作者删除或设为私有，上述集合中可用推文的最终数量可能有所减少；同时为遵守Twitter服务条款，主办方不提供推文的具体内容，可参考2011年与2012年TREC微博追踪任务（TREC Microblog Track）所采用的机制，通过推文标识符获取推文原文。每条推文将被划分至以下声誉维度之一：产品/服务、创新、工作场所、企业公民、治理、领导力、绩效以及未定义（Undefined）。 针对作者画像任务，本次数据集包含超过8000个与汽车、银行领域相关的Twitter账号（所有账号粉丝数均不少于1000）。每个账号包含三类信息：(i) 作者名称；(ii) 账号主页链接；(iii) 爬取时刻作者发布的最新600条推文。声誉领域专家将手动识别意见领袖（即具备声誉影响力的作者），并将其标注为"影响力者（Influencer）"。未被认定为意见领袖的账号将被标记为"非影响力者（Non-Influencer）"。若无法将某一账号归入上述两类，则将其标注为"无法判定（Undecidable）"。 每位意见领袖将被进一步划分为记者、专业人士、权威人士、活动家、投资者、企业或名人。数据集将划分为训练集与测试集，预估划分比例分别为30%与70%，具体划分方案将在后续公布。