# Credibility Analysis on Twitter Considering Topic Detection
## 7 Conclusions and Future Work  
In this work, we extended a credibility model by adding topic analysis for tweets that have hashtags, since currently on Twitter it is very common to use hashtags to somehow label the tweet with words that are trending or relevant. To do so, we first compared different topic detection algorithms; we evaluated them using precision, recall, and F1 score, and we stayed with the one that gave the best results, which was NMF. We can notice that from the 6424 tweets of PHEME dataset that only 1987 have hashtags. That means the other remaining tweets will keep their credibility probability percentage the same as the original model. It can be seen in the 'Ebola' event that since it did not contain any hashtags, the extended model obtained the same results as the original one. Moreover, the dataset with the greatest difference between the models is \"[PERSON]\", due to  
Figure 21: All tweets with hashtags from PHEME dataset by threshold. Figure 20: All Tweets from PHEME dataset by threshold. this event being about stolen works from the Gurlitt Collection (arts collected by [PERSON]); therefore, most of the tweets talk about art and museums, and the hashtags used for this event are also related to these words; for example: \"#Entertainment\", \"#museum\", and \"#art\". The extended model increases, in this event, up to 4.80% the average of F1 score values. In general, the improvement of the extended model in the PHEME dataset can be shown. Although it is not much (up to 3.04% F1 score for the 60% threshold), it is because of the fact that only 30% of the tweets had hashtags; for the case where all tweets have hashtags, the improvement is more significant (up to an F1 score of 9.60%). Even though the NMF was not trained for PHEME dataset, we obtained high F1 score values for events such as Prince Toronto (97.80% F1 score for 80% threshold), Ebola (100% F1 score for thresholds greater than 80%), Germanwings (64.96% F1 score for 90% threshold), Ferguson (approx 39% F1 score for thresholds greater than 75%), and Sydney Siege (51.90% F1 score for 90% threshold). This effect was obtained thanks to the huge number of topics with which the model was trained, returning topics that are related. We are currently working on extending this model by considering retweets, likes, and other attributes to measure the social impact of a tweet, which in turn could improve the measure of credibility, and applying it to other languages, such as Spanish and French. Moreover, we are planning to use a larger dataset to have a greater variety of topics and keywords that can be used for analysis, as well as to evaluate other topic detection algorithms such as neural language models and community detection. Finally, the present study shows the feasibility of integrating a topic analysis to our credibility framework and of considering certain associated semantics. However, this concern is still a challenge. Other semantic aspects can be also incorporated such as the following: How do hashtags impact the credibility human perception of tweets? Is there coherence between the tweet image and its topic? Is the URL associated with the tweet concordant with its topic? Part of these questions will be considered as further works, with additional experiments for qualitative and quantitative assessment. Conceptualization: M.H.-M., A.A., I.D. and Y.C. ; data curation: M.H.-M., A.A., I.D. and Y.C. ; methodology: M.H.-M., A.A., I.D. and Y.C. ; software: M.H.-M., A.A., I.D., J.C.-L. and Y.C. ; investigation: M.H.-M., A.A., I.D. and Y.C. ; writing--original draft preparation: M.H.-M.; writing--review and editing: M.H.-M., A.A., I.D. and Y.C. All authors have read and agreed to the published version of the manuscript. This research was supported by the FONDO NACIONAL DE DESARROLO CIENTIFIC, TECNOLOGICO Y DE INNOVACION TECNOLOGIC--FONDECYT as an executing entity of CONCYTEC under grant agreement no. 01-2019-FONDECYT-BM-INC.INV in the project RUTAS: Robots for Urban Tourism Centers, Autonomous and Semantic-based. Not applicable.