diff --git a/data/xml/2024.arabicnlp.xml b/data/xml/2024.arabicnlp.xml
index 16382a5b37..14d122525f 100644
--- a/data/xml/2024.arabicnlp.xml
+++ b/data/xml/2024.arabicnlp.xml
@@ -1107,7 +1107,9 @@
     </paper>
     <paper id="98">
       <title><fixed-case>ISHFMG</fixed-case>_<fixed-case>TUN</fixed-case> at <fixed-case>S</fixed-case>tance<fixed-case>E</fixed-case>val: Ensemble Method for <fixed-case>A</fixed-case>rabic Stance Evaluation System</title>
+      <author><first>Ammar</first><last>Mars</last><affiliation>Smart Lab Laboratory, Tunis</affiliation></author>
       <author><first>Mustapha</first><last>Jaballah</last><affiliation>Ecole Nationale Supérieure d’ingénieurs de Tunis-ENSIT</affiliation></author>
+      <author><first>Dhaou</first><last>Ghoul</last><affiliation>HF-Lab, HIGHSYS, France</affiliation></author>
       <pages>832-836</pages>
       <abstract>It is essential to understand the attitude of individuals towards specific topics in Arabic language for tasks like sentiment analysis, opinion mining, and social media monitoring. However, the diversity of the linguistic characteristics of the Arabic language presents several challenges to accurately evaluate the stance. In this study, we suggest ensemble approach to tackle these challenges. Our method combines different classifiers using the voting method. Through multiple experiments, we prove the effectiveness of our method achieving significant F1-score value equal to 0.7027. Our findings contribute to promoting NLP and offer treasured enlightenment for applications like sentiment analysis, opinion mining, and social media monitoring.</abstract>
       <url hash="28ec2dc3">2024.arabicnlp-1.98</url>