## 3 Methodology
### Datasets and Pre-Processing  
The data of the research are adopted from [PERSON]'s thesis [3], but for a different purpose: testing a proposed methodology. The data is taken from four UK newspapers: The Telegraph, The Guardian, The Sun, and The Mirror. The dataset consists of articles related to covering the use of chemical weapons in Syria. The concordance lines of the corpus are taken from the occurrences of the strong collocates that are pre-processed with [PERSON]'s thesis. These lines are manually analyzed by [PERSON] according to the themes (for more details about the thematic analysis of the concordance lines, see [PERSON] [3]). The themes are extracted according to their original classification in [PERSON]'s project. Four themes are generated and represent most lines in the dataset. The themes used in the experiment are \"Evaluation of the Syrian situation\", \"International stance\", \"Representation of the UK and USA together\", and \"Local context of the UK\". A total of 1062 lines are extracted that represent the themes, as shown below in Table 1. The total number of lines and tokens for each theme are listed below. In the first theme, the lines focus on evaluating the Syrian situation with either the representation of the chemical attacks or the tragedy in Syria. The second theme focuses on the representation of the UK and USA together, specifically from the perspectives of supporting allies and their political orientations. The third theme represents the UK's political stance in relation to the Syrian chemical attack and the possibility of British military action. The last theme, \"International stance\", highlights the role of international agents such as the United Nations. Table 2 below shows a sample of these lines. In the current study, we suggest that analysts may take a random selection of concordance lines that might be representative of the results. Then, the rest of the concordance lines should be classified electronically according to the manual categorization of the se \begin{table}
\begin{tabular}{c c c} \hline \hline
**Themes** & **Number of Lines** & **Number of Tokens** \\ \hline Evaluation of the Syrian situation & 250 & 8396.0 \\ \hline Representation of the UK and USA together & 264 & 9230.0 \\ \hline Local context of the UK & 250 & 9780.0 \\ \hline International stance & 298 & 10,635.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total number of concordance lines and tokens for each theme. Figure 1: Architecture of the long short-term memory (LSTM) neural network (cited from [25]). \begin{table}
\begin{tabular}{c c} \hline \hline
**Themes** & **Lines** \\ \hline
**Evaluation of the** & It portrays ISIS as the main threat to Syrians, despite [PERSON] \\
**Syrian situation** & killing at least six times more civilians. \\ \hline
**Representation of the UK and** & Saturday August 24: [PERSON] and USA President \\
**USA together** & [PERSON] pledge a “serious response” to the Syria gas \\  & attack after a critical 30 min phone call between the pair at 4.30 pm n which they decide to consider “all options”. \\ \hline
**Local context of the UK** & Downing Street says, after a weekend of briefing that \\  & military intervention may be imminent. \\ \hline
**International stance** & A former UN appeal judge and leading international lawyer, \\  & said yesterday that Russia was wrong to insist that military \\  & intervention would hinge on UN consent. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample of thematic analysis from extracted KWIC. lected analyzed concordance lines. To test this method's validity, we used the same data that were used in [PERSON]'s thesis. The lines are pre-processed with some processing steps. For example, numbers, special characters, stop words, and non-English letters are removed. Then, each line is converted into a sequence of tokens which serves as the input to the neural network. Each token represents a single word converted to a digit, and each digit is used for the training process. We chose the most frequent 5000 words.