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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
<title>Clickbait Challenge</title>
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</button>
<a class="navbar-brand" href="#">Clickbait Challenge</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li><a href="#task">Task</a></li>
<li><a href="#dates">Important Dates</a></li>
<li><a href="#data">Data</a></li>
<li><a href="#software">Submission</a></li>
<li><a href="#workshop">Workshop</a></li>
<li><a href="#results">Results</a></li>
<li><a href="#contact">Organizers</a></li>
</ul>
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<div class="container">
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<div class="col-sm-12">
<div class="page-header">
<h1>Clickbait Challenge <a class="btn btn-default" href="https://goo.gl/7owo61">Register »</a></h1>
<h4>Continuous Shared Task</h4>
</div>
</div>
</div>
<div id="task" class="row">
<div class="col-sm-12">
<h2>Task Description</h2>
<p>We invite you to participate in our ongoing challenge on the detection of clickbait posts in social media. <i>Clickbait</i> refers to social media posts that are, at the expense of being informative and objective, designed to entice its readers into clicking an accompanying link. <a href="#related-work">More on clickbait.</a>
<br>The task of the challenge is to develop a classifier that rates how click baiting a social media post is. For each social media post, the content of the post itself as well as the main content of the linked target web page are provided as JSON-Objects in our <a href="#data">datasets</a>.
</p>
<div class="row">
<div class="col-sm-6">
<pre><code>
{
"id": "608999590243741697",
"postTimestamp": "Thu Jun 11 14:09:51 +0000 2015",
"postText": ["Some people are such food snobs"],
"postMedia": ["608999590243741697.png"],
</code></pre>
<span class="code-caption">instances.jsonl</span>
</div>
<div class="col-sm-6">
<pre><code>
"targetTitle": "Some people are such food snobs",
"targetDescription": "You'll never guess one...",
"targetKeywords": "food, foodfront, food waste...",
"targetParagraphs": [
"What a drag it is, eating kale that isn't ...",
"A new study, published this Wednesday by ...",
...],
"targetCaptions": ["(Flikr/USDA)"]
} </code></pre>
<span class="code-caption">instances.jsonl (cont'd)</span>
</div>
</div>
<p>
Classifiers have to output a clickbait score in the range [0,1], where a value of 1.0 denotes that a post is heavily click baiting.
</p>
<div>
<pre><code>
{"id": "608999590243741697", "clickbaitScore": 1.0}
</code></pre>
<span class="code-caption">results.jsonl</span>
</div>
<p style="clear:both;">
Performance is measured against a crowd-sourced test set. The posts in the training and test sets have been judged on a 4-point scale [0, 0.3, 0.66, 1] by at least five annotators.
</p>
<div class="row">
<div class="col-sm-6">
<!--<div style="text-align:center; font-size: 16px; padding-bottom: 10px;"> How click baiting is the following post?</div>-->
<div class="box_tweet">
<span style="font-size: 24px">Some people are such food snobs <a href="http://wapo.st/1IJi2iv">link</a></span>
</div>
<div class="row">
<div class="col-xs-3">
<div class="radio"><label><input name="score" type="radio">not click baiting (0.0)</label></div>
</div>
<div class="col-xs-3">
<div class="radio"><label><input name="score" type="radio">slightly click baiting (0.33)</label></div>
</div>
<div class="col-xs-3">
<div class="radio"><label><input name="score" type="radio" checked>considerably click baiting (0.66)</label></div>
</div>
<div class="col-xs-3">
<div class="radio"><label><input name="score" type="radio">heavily click baiting (1.0)</label></div>
</div>
</div>
<span class="code-caption">crowd sourcing task design</span>
</div>
<div class="col-sm-6">
<pre><code>
{"id": "608999590243741697",
"truthJudgments": [0.33, 1.0, 1.0, 0.66, 0.33],
"truthMean" : 0.6666667,
"truthMedian": 0.6666667,
"truthMode" : 1.0,
"truthClass" : "clickbait"}
</code></pre>
<span class="code-caption">truth.jsonl</span>
</div>
</div>
<p>
As primary evaluation metric, Mean Squared Error (MSE) with respect to the mean judgments of the annotators is used. For informational purposes, we compute further evaluation metrics such as the Median Absolute Error (MedAE), the F1-Score (F1) with respect to the truth class, as well as the runtime of the classification software. For your convenience, you can <a href="eval.py"> download the official python evaluation program</a>.
</p>
<table class="table table-condensed table-striped">
<thead>
<tr>
<th>MSE</th><th>MedAE</th><th>ACC</th><th>F1</th><th>Runtime</th><th>Team</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>0.024</strong></td><td>0.174</td><td>0.91</td><td>0.760</td><td>17:11:16</td><td><strong>Team 1</strong></td>
</tr>
<tr>
<td><strong>0.052</strong></td><td>0.201</td><td>0.88</td><td>0.533</td><td>02:47:50</td><td><strong>Team 2</strong></td>
</tr>
</tbody>
</table>
</div>
<div id="instructions" class="col-sm-12">
<h3>How To Participate</h3>
<ol class="col-sm-6">
<li><a href="https://goo.gl/7owo61">Register</a> for the challenge to get a TIRA virtual machine.</li>
<li>Develop and train a clickbait classifier on the <a href="#data">training data</a>.</li>
<li><a href="virtual-machine-user-guide.pdf">Deploy</a> the trained classifier on the TIRA virtual machine assigned to you.</li>
</ol>
<ol class="col-sm-6">
<li>Use <a href="http://tira.io">tira.io</a> to self-evaluate the deployed classifier on the test set.</li>
<li>Write and <a href="#paper">submit a paper</a> that describes how you approached the task.</li>
<li>Pesent your approach at the next <a href="#workshop">workshop</a>.</li>
</ol>
</div>
<!--<div class="col-sm-12">
<h3 id="dates">Important Dates</h3>
<div class="col-sm-6">
<table class="timetable table"><tbody>
<tr>
<td>March 31, 2017</td><td>Registration begins.</td>
</tr><tr>
<td>March 31, 2017</td><td>Release of training dataset.</td>
</tr><tr>
<td>May 31, 2017</td><td>Release of validation dataset.</td>
</tr><tr>
<td>July 10 -- August 31, 2017</td><td>Software evaluation phase.</td>
</tr><tr>
<td>September 08, 2017</td><td>Result Notification.</td>
</tr>
</tbody></table>
</div>
<div class="col-sm-6">
<table class="timetable table"><tbody>
<tr>
<td>September 29, 2017</td><td>Paper submission deadline.</td>
</tr><tr>
<td>October 13, 2017</td><td>Notification of acceptance.</td>
</tr><tr>
<td>October 27, 2017</td><td>Camera-ready deadline.</td>
</tr><tr>
<td>November 27, 2017</td><td>Workshop.</td>
</tr>
</tbody></table>
</div>
</div>-->
<div id="related-work" class="col-sm-12">
<h3>Related Work</h3>
<ul>
<li>
<p>Wikipedia. <a href="https://en.wikipedia.org/wiki/Clickbait">Clickbait --- Wikipedia, The Free Encyclopedia</a>. 2017.</p>
<blockquote>Clickbait tweets typically aim to exploit the "curiosity gap", providing just enough information to make readers curious, but not enough to satisfy their curiosity without clicking through to the linked content.</blockquote>
</li>
<li>
<p>Alex Peysakhovich and Kristin Hendrix. <a href="http://newsroom.fb.com/news/2016/08/news-feed-fyi-further-reducing-clickbait-in-feed/">News Feed FYI: Further Reducing Clickbait in Feed</a>. In Facebook newsroom, August, 2016.</p>
<blockquote> A tweet is Clickbait if (1) the tweet withholds information required to understand what the content of the article is; and if (2) the tweet exaggerates the article to create misleading expectations for the reader.</blockquote>
</li>
<li>
<p>Ricardo Bilton. <a href="http://digiday.com/media/many-many-ways-publishers-define-clickbait/">The many different ways publishers define 'clickbait'</a>. In digiday UK, November, 2014.</p>
<blockquote>Clickbait is saying "this town" or "this state" or "this celebrity" instead of saying Los Angeles or Colorado or Justin Timberlake. It's over-promising and under-delivering. It's leaving out the one crucial piece of information the reader may want to know.</blockquote>
</li>
<li>
<p>Martin Potthast, Sebastian Köpsel, Benno Stein, and Matthias Hagen. <a href="http://www.uni-weimar.de/medien/webis/publications/papers/stein_2016b.pdf">Clickbait Detection</a>. In Advances in Information Retrieval (ECIR 16), March 2016. [<a class="bib" href="http://www.uni-weimar.de/medien/webis/publications/bibentries.php?bibkey=stein_2016b">bib</a>]</p>
<blockquote>This paper presents the first machine learning approach to clickbait detection: the goal is to identify messages in a social stream that are designed to exploit cognitive biases to increase the likelihood of readers clicking an accompanying link.</blockquote>
</li>
</ul>
</div>
</div><!--/.row -->
<hr id="data">
<div class="row">
<div class="col-sm-12">
<h2>Datasets</h2>
<p> You can find the datasets for the clickbait challenge by following <a href="https://webis.de/data/webis-clickbait-17">this link</a>. The dataset is provided as a zip archive with the following resources (the unlabeled dataset lacks the truth file):</p>
<ul>
<li> <code>instances.jsonl</code>: A line delimited JSON file (<a href="http://jsonlines.org/">JSON Lines</a>). Each line is a JSON-Object containing the information we extracted for a specific post and its target article. Have a look at the <a href="clickbait17-dataset-schema.txt">dataset schema file</a> for an overview of the available fields.</li>
<li> <code>truth.jsonl</code>: A line delimited JSON file. Each line is a JSON-Object containing the crowdsourced clickbait judgements of a specific post. Have a look at the <a href="clickbait17-dataset-schema.txt">dataset schema file</a> for an overview of the available fields.</li>
<li> <code>media/</code>: A folder that contains all the images referenced in the <code>instances.jsonl</code> file.
</ul>
</div>
</div>
<hr id="software">
<div class="row">
<div class="col-sm-12">
<h2>Software Submission</h2>
<p>We use the Evaluation as a Service platform <a href="http://www.tira.io">TIRA</a> to evaluate the performance of your classifier. TIRA requires that you deploy your classifier as a program that can be executed with two arguments for input and output directories via a command line call. E.g., the syntax could be:</p>
<pre> > myClassifier <b>-i</b> path/to/input/directory <b>-o</b> path/to/output/directory</pre>
<span class="code-caption">example command line call for tira.io</span>
<p> At runtime, the input directory contains the unzipped dataset (i.e. <code>instances.jsonl</code> and <code>media/</code> folder) your classifier has to process. The predictions of your classifier should be written into a file called <code>results.jsonl</code> into the given output directory. The <code>results.jsonl</code> file should contain a valid JSON-Object in each line that contains the <i>id</i> and the predicted <i>clickbaitScore</i> for a post (cf. the <a href="clickbait17-dataset-schema.txt">dataset schema file</a>).</p>
<div>
<pre><code>
{"id": "608999590243741697", "clickbaitScore": 1.0}
{"id": "609408598704128000", "clickbaitScore": 0.25}
...
</code></pre>
<span class="code-caption">results.jsonl</span>
</div>
<p>We will ask you to deploy your classifier onto a virtual machine that will be made accessible to you after registration. You can choose freely among the available programming languages and among the operating systems Microsoft Windows and Ubuntu. You will be able to reach the virtual machine via ssh and via remote desktop. More information about how to access the virtual machines can be found in the user guide below:</p>
<p><a class="btn btn-default" href="virtual-machine-user-guide.pdf">Virtual Machine User Guide »</a></p>
<p>Once deployed on your virtual machine, we ask you to access TIRA at <a href="http://www.tira.io">www.tira.io</a>, where you can self-evaluate your software on the test data.</p>
<p><strong>Note:</strong> By submitting your software you retain full copyrights. You agree to grant us usage rights only for the purpose of the Clickbait Challenge. We agree not to share your software with a third party or use it for other purposes than the Clickbait Challenge.</p>
</div>
</div>
<hr id="paper">
<div class="row">
<div class="col-sm-12">
<h2>Paper Submission</h2>
<ol>
<li>Prepare a paper about your approach and its variants using our <a href="http://www.clickbait-challenge.org/clickbait17-notebook-template.zip">paper template</a>.</li>
<li>Publish the finished paper on <a href="https://arxiv.org/corr">arXiv</a>, and <a href="mailto:[email protected]">let us know</a> where we can find it.</li>
<li>Publish your code on <a href="https://github.com/clickbait-challenge">our github repository</a>. We can fork a repo you already have, or create a new one for you and invite you as owners.</li>
</ol>
</div>
</div>
<hr id="workshop">
<div class="row">
<div class="col-sm-12">
<h2>Workshop</h2>
<p>
The first workshop on clickbait detection took place on November 27, 2017 at Bauhaus-Universität Weimar, Germany.
</p>
<p class="row">
<h4>Location</h4>
<div class="col-sm-6">
<div><a href="https://www.uni-weimar.de/de/medien/institute/digital-bauhaus-lab/about/">Digital Bauhaus Lab</a></div>
<div>Bauhausstr. 9a, 3rd floor</div>
<div>99423 Weimar </div>
<div>Germany </div>
</div>
<div class="col-sm-6">
<a href="https://www.uni-weimar.de/fileadmin/user/fak/medien/institute/Digital_Bauhaus_Lab/Lageplan_Universitaet_DBL_englisch.pdf"><img src="https://www.uni-weimar.de/fileadmin/_processed_/e/1/csm_DBL_Web_lageplan_8fa1c0d9dd.png"></a>
</div>
</p>
<p>
<h4> Schedule </h4>
<table class="table table-striped table-condensed">
<thead><tr><th colspan="2"><span style="display:none">.</span></th></tr></thead>
<tfoot><tr><td colspan="2"><span style="display:none">.</span></td></tr></tfoot>
<tbody>
<tr><td>09:00 - 09:30</td><td>Welcome Reception</td></tr>
<tr><td>09:30 - 10:30</td><td>Clickbait-Challenge 2017: Overview<br/><i>Martin Potthast, Tim Gollub</i></td></tr>
<tr><td>10:30 - 11:00</td><td>A Neural Clickbait Detection Engine<br/><i>Yash Kumar Lal</i></td></tr>
<tr><td>11:00 - 11:30</td><td>Clickbait Identification using Neural Networks<br/><i>Philippe Thomas</i></td></tr>
<tr><td>11:30 - 12:00</td><td>The Emperor Clickbait Detector<br/><i>Erdan Genc</i></td></tr>
<tr><td>12:00 - 14:00</td><td>Lunch Break</td></tr>
<tr><td>14:00 - 14:30</td><td>Detecting Clickbait in Online Social Media: You Won’t Believe How We Did It<br/><i>Aviad Elyashar</i></td></tr>
<tr><td>14:30 - 15:00</td><td>Heuristic Feature Selection for Clickbait Scoring<br/><i>Matti Wiegmann</i></td></tr>
<tr><td>15:00 - 16:00</td><td>Discussion and Outlook</td></tr>
</tbody>
</table>
</p>
</div>
</div>
<hr id="results">
<div class="row">
<div class="col-sm-12">
<h2>Results</h2>
<p>
The following list presents the current performances achieved by the participants. As primary evaluation measure, Mean Squared Error (MSE) with respect to the mean judgments of the annotators is used. For further metrics, see the <a href="http://www.tira.io/task/clickbait-detection/dataset/clickbait17-test-170720/">full result table on tira.io</a>. If provided, paper and code of the submissions are linked in each row.
</p>
<table class="table table-striped">
<thead>
<tr>
<th>MSE</th><th>F1</th><th>Prec</th><th>Rec</th><th>Acc</th><th>Runtime</th><th>Team</th><th>Paper/Code</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>0.032</strong></td><td>0.670</td><td>0.732</td><td>0.619</td><td>0.855</td><td>00:01:10</td><td><strong>albacore</strong></td><td><a href="papers/omidvar18-notebook.pdf">paper</a> <a href="https://github.com/clickbait-challenge/albacore">code</a></td>
</tr>
<tr>
<td><strong>0.033</strong></td><td>0.683</td><td>0.719</td><td>0.650</td><td>0.856</td><td>00:03:27</td><td><strong>zingel</strong></td><td><a href="papers/zhou17-notebook.pdf">paper</a> <a href="https://github.com/clickbait-challenge/zingel">code</a></td>
</tr>
<tr>
<td><strong>0.034</strong></td><td>0.679</td><td>0.717</td><td>0.645</td><td>0.855</td><td>00:07:20</td><td><strong>anchovy</strong></td><td><a href="https://github.com/clickbait-challenge/anchovy">code</a></td>
</tr>
<tr>
<td><strong>0.036</strong></td><td>0.641</td><td>0.714</td><td>0.581</td><td>0.845</td><td>00:04:03</td><td><strong>emperor</strong></td><td> <a href="https://github.com/clickbait-challenge/emperor">code</a></td>
</tr>
<tr>
<td><strong>0.036</strong></td><td>0.036</td><td>0.728</td><td>0.568</td><td>0.847</td><td>00:08:05</td><td><strong>carpetshark</strong></td><td><a href="papers/grigorev17-notebook.pdf">paper</a> <a href="https://github.com/clickbait-challenge/carpetshark">code</a></td>
</tr>
<tr>
<td><strong>0.039</strong></td><td>0.656</td><td>0.659</td><td>0.654</td><td>0.837</td><td>00:35:24</td><td><strong>arowana</strong></td><td></td>
</tr>
<tr>
<td><strong>0.041</strong></td><td>0.631</td><td>0.642</td><td>0.621</td><td>0.827</td><td>00:54:28</td><td><strong>pineapplefish</strong></td><td><a href="papers/glenski17-notebook.pdf">paper</a></td>
</tr>
<tr>
<td><strong>0.043</strong></td><td>0.565</td><td>0.699</td><td>0.474</td><td>0.826</td><td>00:04:31</td><td><strong>whitebait</strong></td><td><a href="papers/thomas17-notebook.pdf">paper</a> <a href="https://github.com/clickbait-challenge/whitebait">code</a></td>
</tr>
<tr>
<td><strong>0.044</strong></td><td>0.552</td><td>0.758</td><td>0.434</td><td>0.832</td><td>00:37:34</td><td><strong>clickbait17-baseline</strong></td><td></td>
</tr>
<tr>
<td><strong>0.045</strong></td><td>0.604</td><td>0.711</td><td>0.524</td><td>0.836</td><td>01:04:42</td><td><strong>pike</strong></td><td><a href="papers/cao17-notebook.pdf">paper</a> </td>
</tr>
<tr>
<td><strong>0.046</strong></td><td>0.654</td><td>0.654</td><td>0.653</td><td>0.835</td><td>06:14:10</td><td><strong>tuna</strong></td><td><a href="papers/gairola17-notebook.pdf">paper</a></td>
</tr>
<tr>
<td><strong>0.079</strong></td><td>0.650</td><td>0.530</td><td>0.841</td><td>0.785</td><td>00:04:55</td><td><strong>torpedo</strong></td><td><a href="papers/indurthi17-notebook.pdf">paper</a> <a href="https://github.com/clickbait-challenge/torpedo">code</a></td>
</tr>
<tr>
<td><strong>0.099</strong></td><td>0.023</td><td>0.779</td><td>0.012</td><td>0.764</td><td>00:26:38</td><td><strong>houndshark</strong></td><td></td>
</tr>
<tr>
<td><strong>0.118</strong></td><td>0.467</td><td>0.380</td><td>0.605</td><td>0.671</td><td>00:05:00</td><td><strong>dory</strong></td><td></td>
</tr>
<tr>
<td><strong>0.174</strong></td><td>0.261</td><td>0.167</td><td>0.593</td><td>0.209</td><td>114:04:50</td><td><strong>salmon</strong></td><td><a href="papers/elyashar17-notebook.pdf">paper</a></td>
</tr>
<tr>
<td><strong>0.252</strong></td><td>0.434</td><td>0.287</td><td>0.893</td><td>0.446</td><td>19:05:31</td><td><strong>snapper</strong></td><td><a href="papers/papadopoulou17-notebook.pdf">paper</a> <a href="https://github.com/clickbait-challenge/snapper">code</a></td>
</tr>
</tbody>
</table>
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<li>Tim Gollub, Bauhaus-Universität Weimar.</li>
<li>Martin Potthast, Bauhaus-Universität Weimar.</li>
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<li>Matthias Hagen, Bauhaus-Universität Weimar.</li>
<li>Benno Stein, Bauhaus-Universität Weimar.</li>
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