PAN 22 Author Profiling: Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO)

<strong>TASK</strong> With irony, language is employed in a figurative and subtle way to mean the opposite to what is literally stated. In case of sarcasm, a more aggressive type of irony, the intent is to mock or scorn a victim without excluding the possibility to hurt. Stereotypes are often used, especially in discussions about controversial issues such as immigration or sexism and misogyny. At PAN’22, we will focus on profiling ironic authors in Twitter. Special emphasis will be given to those authors that employ irony to spread stereotypes, for instance, towards women or the LGTB community. The goal will be to classify authors as ironic or not depending on their number of tweets with ironic content. Among those authors we will consider a subset that employs irony to convey stereotypes in order to investigate if state-of-the-art models are able to distinguish also these cases. Therefore, given authors of Twitter together with their tweets, the goal will be to profile those authors that can be considered as ironic. <strong>DATA</strong> <strong>Input</strong> The uncompressed dataset consists in a folder which contains: A XML file per author (Twitter user) with 200 tweets. The name of the XML file correspond to the unique author id. A truth.txt file with the list of authors and the ground truth. The format of the XML files is: <pre><code class="language-xml"> &lt;author lang="en"&gt; &lt;documents&gt; &lt;document&gt;Tweet 1 textual contents&lt;/document&gt; &lt;document&gt;Tweet 2 textual contents&lt;/document&gt; ... &lt;/documents&gt; &lt;/author&gt;</code></pre> The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label. <pre><code> 2d0d4d7064787300c111033e1d2270cc:::I b9eccce7b46cc0b951f6983cc06ebb8:::NI f41251b3d64d13ae244dc49d8886cf07:::I 47c980972060055d7f5495a5ba3428dc:::NI d8ed8de45b73bbcf426cdc9209e4bfbc:::I 2746a9bf36400367b63c925886bc0683:::NI ...</code></pre> <strong>Evaluation</strong> The performance of your system will be ranked by accuracy. More info on the task: https://pan.webis.de/clef22/pan22-web/author-profiling.html<strong>任务</strong>：反讽（irony）是一种采用修辞化且隐晦的语言手段，用以表达与字面陈述相反的内涵。讽刺（sarcasm）作为更为激进的反讽类型，其意图在于嘲弄或讥讽表达对象，且不排除造成伤害的可能性。这类表达常借助刻板印象（stereotypes），尤其在移民、性别歧视与厌女等争议性议题的讨论中更为常见。在PAN 2022赛事中，我们将聚焦于推特（Twitter）平台上的反讽作者画像任务。本次任务将重点关注那些借助反讽传播刻板印象的作者，例如针对女性或LGTB群体的作者。任务目标为：根据作者所发布的含反讽内容的推文数量，将其划分为反讽作者与非反讽作者两类。此外，我们将从反讽作者中选取一个子集，这些作者通过反讽传递刻板印象，以探究当前最优模型（state-of-the-art models）是否能够区分此类案例。综上，给定推特作者及其推文数据，任务目标为识别出可被归类为反讽作者的用户。 <strong>数据</strong> <strong>输入</strong>：未压缩的数据集以文件夹形式存储，包含以下内容：每位推特用户（作者）对应一个XML文件，内含该用户发布的200条推文；XML文件的文件名即为该作者的唯一标识符。此外还包含一个`truth.txt`文件，记录所有作者的真实标签（ground truth）。 XML文件的格式如下： <pre><code class="language-xml">&lt;author lang="en"&gt; &lt;documents&gt; &lt;document&gt;Tweet 1 textual contents&lt;/document&gt; &lt;document&gt;Tweet 2 textual contents&lt;/document&gt; ... &lt;/documents&gt; &lt;/author&gt;</code></pre> `truth.txt`文件的格式如下，其中第一列为作者唯一标识符，第二列为其真实标签： <pre><code>2d0d4d7064787300c111033e1d2270cc:::I b9eccce7b46cc0b951f6983cc06ebb8:::NI f41251b3d64d13ae244dc49d8886cf07:::I 47c980972060055d7f5495a5ba3428dc:::NI d8ed8de45b73bbcf426cdc925886bc0683:::I 2746a9bf36400367b63c9258886bc0683:::NI ...</code></pre> <strong>评估</strong>：系统的性能将以准确率（accuracy）进行排名。更多任务相关信息可参考：https://pan.webis.de/clef22/pan22-web/author-profiling.html