Papers by Ahmet Aker
Can Rumour Stance Alone Predict Veracity? (C18-1)
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| Challenge: | Existing studies of automatic veracity classification of social media rumours have not explored the effectiveness of crowd stance to determine veracity. |
| Approach: | They propose to use stance as an additional feature to those commonly used in earlier studies to model the veracity of a rumour using Hidden Markov Models and collective stance information to model a social media rumor. |
| Outcome: | The proposed models outperform those using crowd stance and tweets’ times as the only features for modelling true and false rumours. |
Multi-lingual Argumentative Corpora in English, Turkish, Greek, Albanian, Croatian, Serbian, Macedonian, Bulgarian, Romanian and Arabic (L18-1)
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Alfred Sliwa, Yuan Ma, Ruishen Liu, Niravkumar Borad, Seyedeh Ziyaei, Mina Ghobadi, Firas Sabbah, Ahmet Aker
| Challenge: | Argumentative corpora are costly to create and available only in few languages with English dominating the area. |
| Approach: | They use 8 different argument mining classifiers trained for English to build a parallel corpora in which the source language is English and the target language is either a Balkan language or Arabic. |
| Outcome: | The proposed method is based on 8 different argument mining classifiers trained for English and project the decision to the target language. |