# Are Strong Baselines Enough? False News Detection with Machine Learning
## 4 Discussion
#### Linguistic Analysis of Language Used in False News  
To understand false news better, this study concluded with a simple linguistic analysis of two datasets--the LIAR dataset [4] and the novel ChatGPT-generated dataset. Mainly, we looked at the 20 most common words found in the two datasets, as well as their TF-IDF scores and N-gram frequencies. This way, we can observe the keywords of the datasets, as well as the impact of the words when performing automated detection. From these results, we can also observe whether the language used in false news is different when they are generated by humans or by a computer. The most common words for the LIAR dataset [4] are presented in a word cloud that can be observed in Figure 20. The most common word that appeared on this dataset was _obama_, with a TF-IDF score of 44.67069 and an N-gram frequency of 271. The TF-IDF scores for other words can be seen in Table 25 and the N-gram frequencies in Table 26.  
Figure 20: Twenty most common words from LIAR dataset [4]. The most common words for the ChatGPT-generated dataset can be observed in Figure 21. The most common word that appeared on this dataset was _potential_, with a TF-IDF score of 3.52128 and an N-gram frequency of 262. The TF-IDF scores for other words can be seen in Table 27 and the N-gram frequencies in Table 28.  
When comparing the vocabulary of the two datasets, we can observe that the words appearing in the LIAR dataset [4] are more eye-catching than in the ChatGPT dataset. The words appearing on the ChatGPT dataset tend to be more neutral and do not show as much insight into the actual nature of the generated articles inside the dataset. This observation could argue that using tools like ChatGPT can be very efficient in making false news articles appear more similar in vocabulary to real news articles. \begin{table}
\begin{tabular}{c c} \hline \hline
**Word** & **N-Gram Frequency** \\ \hline
**backyard** & 1 \\
**gardening** & 1 \\
**revolution** & 3 \\
**regulate** & 3 \\
**safety** & 5 \\
**personal** & 8 \\
**taxpayer** & 15 \\
**funded** & 14 \\
**food** & 20 \\
**day** & 26 \\
**legislation** & 27 \\
**administration** & 40 \\
**even** & 45 \\
**stimulus** & 41 \\
**raise** & 40 \\
**pay** & 65 \\
**voted** & 91 \\
**new** & 135 \\
**will** & 158 \\
**obama** & 271 \\ \hline \hline \end{tabular}
\end{table} 
Table 26: N-gram frequencies of 20 most common words from LIAR dataset [4]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Word** & **TF-IDF Score** \\ \hline
**backyard** & 0.44503 \\
**gardening** & 0.44503 \\
**revolution** & 0.96835 \\
**regulate** & 1.13286 \\
**safety** & 1.66006 \\
**personal** & 2.26182 \\
**taxpayer** & 3.97960 \\
**funded** & 4.05192 \\
**food** & 5.22986 \\
**day** & 5.82952 \\
**legislation** & 6.16053 \\
**administration** & 8.80094 \\
**even** & 9.62488 \\
**stimulus** & 10.08428 \\
**raise** & 10.20364 \\
**pay** & 14.15832 \\
**voted** & 19.63578 \\
**new** & 24.33575 \\
**will** & 26.08082 \\
**obama** & 44.67069 \\ \hline \hline \end{tabular}
\end{table} 
Table 25: TF-IDF scores of 20 most common words from LIAR dataset [4]. Figure 21: Twenty most common words from ChatGPT-generated dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Word** & **TF-IDF Score** \\ \hline
**meanwhile** & 0.27872 \\
**serve** & 0.76586 \\
**claim** & 1.02257 \\
**continue** & 1.28523 \\
**challenge** & 1.15479 \\
**remain** & 1.64038 \\
**individual** & 1.69804 \\
**new** & 1.70962 \\
**event** & 1.71798 \\
**future** & 1.90560 \\
**importance** & 1.98240 \\
**may** & 2.35172 \\
**within** & 2.36911 \\
**life** & 2.41739 \\
**public** & 2.67856 \\
**will** & 3.18160 \\
**world** & 3.38626 \\
**human** & 3.42755 \\
**potential** & 3.52128 \\
**incident** & 3.87482 \\ \hline \end{tabular}
\end{table} 
Table 27: TF-IDF scores of 20 most common words from ChatGPT-generated dataset.