-       Good trade partners 

-       Own energy resource provisions 

 

-       Lack of independence 

-       Lack of unity between member states 

-       Bad leadership 

 

The above themes have been thought of deductively however due to the nature of the research question, it would be necessary to go through the data and analyse the documents and see which themes have emerged regarding European energy policy. Based on the sentiment positive and negative, further sub categories would be coded to understand the themes which the Russian State aims to push forward and portray to the EU. The themes in this example are be mutually exclusive, however greater context needs to be considered.

 

How have the narratives contained within the British media surrounding Russian influence in the Sahel changed with the Russian invasion of Ukraine?

 

Hypothesis: the narratives surrounding Russian influence in the Sahel became more negative and frequent post-invasion of Ukraine.

 

 

My sample would consist of cartoons, opinion pieces, and articles contained within the broadsheets either side of 24^th February 2022.  Such papers include The Times, Financial Times, The Observer, The Daily Telegraph, and The Sunday Times.  I would stick to the broadsheets due to their reputation for more serious, intellectual journalism but this could certainly be expanded to include wider newspapers including the tabloids if the initial sample size proved too small.  

 

 

This coding scheme was created inductively.  It would feature positive and negative categories looking for the tones of articles speaking of Russian nationals such as prominent oligarchs.  Moreover, it will look at the imagery surrounding Putin, the Russian ruling elite, and Russia as a nation, directly comparing cartoons and photographs included in articles from pre and post-invasion.  Moreover, to research the hypothesis, I propose looking for key related words and the frequency of their employment in the media both prior to the Russian invasion and in the months following.  For example, if we track the use of words such as ‘pariah’, ‘barbaric’, ‘unprovoked invasion’, and ‘Russian aggression’ we can compare their employment pre and post-24^th February and will likely find support for the hypothesis.  

 

Building on this foundation, I would then search for the number of articles in the above media related to the Sahel prior to and post-invasion.  Before repeating the process regarding certain key words.

Images and texts from Le Monde, Le Figaro, die Welt, die Tageszeitung.

Code : Inductively from news’ article

Image

Positive : 

-looking at the camera

-in her job

Negative : 

-looking down 

Tone : 

-positive : well-dressed, confident

-negative : badly prepared, bad posture

 

Text: frequency of words

'Defeat'; 'Lost'; 'Failed'

Tone: negative adjective on her personality. 

→ deductive analysis, requiring additional interpretation of German words

→ dataset: Newspaper headlines in the three biggest German newspapers (Frankfurter Allgemeine Zeitung (FAZ, more liberal), Bild (more right wing), Zeit (more left wing)) in 2015/16 vs 2022

→ Positive:

→ Negative:

In both cases, subcategories would be added to analyse possibly changes over time within the different time and dataframes.

Hypothesis: hypothesis is that NATO will be portrayed in a positive light (as in people either support it or acknowledge it as more effective) after the start of the Ukraine War begins (As war started, it makes NATO (which is a self defence pact between states) more attractive or useful).

Another Hypothesis that I want to sustain is that the US in particular will be referenced more so in relation to positive support for NATO (As their claims the war would happen became true).

Coding Scheme:

Supportive of NATO - Tweet presents NATO in a positive light (could be it being declared as strong, effective or necessary) or defends NATO from attacks (in debate).

Negative of NATO - Attacking Nato in any way (stating it is weak, it is not effective, it doesn't have a concrete goal (since fall of USSR) or repeating and supporting Kremlin rhetoric of it antagonising Russia).

Mixed/Neutral tone - No discernible message or tone put forth.

Tweets mentioning US - Any tweets that in any way refer to the US (politically) when stating a position on NATO (positive or negative).

 

-        Gender (male/female)

 

-        Adult or Children

 

-        State of vulnerability

-        Emotion (happiness/sadness)

-       Rational

-       Calm

-       Intelligent

-       Reliable

-       Emotional

-       Egoistic

-       Stubborn

-       Passive

1.Compared to reports on traffic accidents committed by males, words in reports on traffic accidents committed by females are more likely to refer to the offenders’ gender. 

 

2.Compared to reports on traffic accidents committed by males, images in reports on traffic accidents committed by females are more likely to focus on offender.

 (Gender here indicates biological gender)

 

 

 The past ten years (2012-2022)

 Reports in Chinese traffic polices’ Weibo official accounts 

 

Unit of Analysis: Full reports (words and photographs)

 

 

1. Gender of the offenders (binary variable: 1 for female, 0 for male)

2. Whether the reports mention the offenders’ gender for more than one time? (1 for True, 0 for False)

3.
3.1 Whether the photographs of accidents in the reports focus on the accident itself (like the damaged car) or the offender? 
3.2 And if it focuses on the latter one, what is the offender's gender(biological)? (binary variable: 1 for female, 0 for male)

-success 

-effective 

-general vibe 

Neutral: 

Negative:

-dependency 

-crisis 

-potentially xenophobic terms