Narrative Resilience in Online Conflict Coverage: Insights from Twitter Analysisby@memeology
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Narrative Resilience in Online Conflict Coverage: Insights from Twitter Analysis

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The study explores Twitter's role in memetic warfare, highlighting the effectiveness of clear narratives and audience engagement. It discusses identity building, political responses, and ethical considerations in online conflict coverage, providing valuable insights into communication strategies and audience dynamics.
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(1) Yelena Mejova, ISI Foundation, Italy;

(2) Arthur Capozzi, Università degli Studi di Torino, Italy;

(3) Corrado Monti, CENTAI, Italy;

(4) Gianmarco De Francisci Morales, CENTAI, Italy.


Background and Related Work



Discussion and References


The results of this study have important implications within the theoretical frameworks outlined in Section 2. The international reach of the content studied here proves the efficacy of using Twitter for a communication campaign, especially if it aims to reach Western countries where the platform’s penetration is substantial. Content that contains clear narratives proves to be especially popular, and in particular, tweets with a victim narrative receive double the number of retweets. Using multimedia to advocate a victim’s position may be especially effective, as visual depictions

of people in need are known to activate brain circuits associated with facial and affective processing, which can compel people to give more help to strangers [24]. As social media is increasingly used to mobilize support and ask for charitable giving [9, 65], narrative emphasis may be an important variable to control in communication campaigns. Interestingly, the preference for the victim narrative by the overall audience captured here does not translate to the actions of countries, as it is negatively associated with per-capita financial assistance to Ukraine. Instead, countries that contribute more to Ukraine’s war effort resonate more with malevolent narratives: villain and fool. This phenomenon can be understood by considering that their support for Ukraine might be driven by their view of Russia as a threat, given their shared history and physical proximity. Indeed, soon after the invasion, Finland has reversed a long-time policy of neutrality and applied for membership to NATO on 18 May 2022, which was granted on 4 April 2023. In this sense, the propagation of the content posted by the examined Ukrainian accounts reflects politically relevant signals.

Another theoretical lens is that of identity building through narratives—a process that has been studied widely by the CHI community. For instance, Das and Semaan [15] postulate that using a social media platform as a space to address colonial grievances and build a self-concept fosters a “narrative resilience”, which serves as a “collective and reflexive mechanism through which people work to generate resilience”. During the annotation process, we encountered many examples of Laenui [38]’s decolonization phases, including mourning, commitment, and calls to action, and yet other emphases may be present as the conflict develops and hopefully ends (although applying the decolonization lens to the Russo-Ukrainian conflict is controversial). Further, narrative building has been used to engage marginalized communities [50], strengthen their voice [27], and promote conflict reconciliation [60]—tasks that may become important towards the end of the conflict. Recently, social media narratives have been shown to provide Ukrainian refugees a way to maintain an “emotional, dynamic, and constantly updating” bond with their homeland, and a space for the “negotiation and performance of ethnic and national identities” [37]. Although qualitative analysis of identity building was not a focus of the present study, examining the narratives that establish the self-concept of Ukraine as an independent state, and its relationship with Russia, the West, and the rest of the world, is an important future research direction.

The insights of this study are limited by its unique setting: the time period of the Russian invasion, the selection of the accounts, the peculiar affordances the Twitter platform presents, and the uneven adoption of the platform around the world. Nevertheless, our finding that users from countries that support Ukraine the most also retweet the content from major Ukrainian accounts at a higher rate suggests a synchrony between the supportive actions of Twitter users (which could be considered to be a form of slacktivism [40]) and concrete financial decisions by the governments of their countries. Conversely, the responses to the Eurobarometer survey question on sending help to Ukraine had no significant correlation with the propagation of the Ukrainian tweets, which suggests that users on Twitter are more “in tune” with the decisions of their governments than a representative population sample (indeed, the responses in the Eurobarometer are not correlated to financial aid for the 19 European countries in our data). It is possible that the retweeters include government entities and actors, as well as automated accounts. Unfortunately, we are unable to apply the standard bot detection techniques [16] as the platform closed down its API in early 2023. However, the effect of such accounts should be limited in a retweet analysis as each account may retweet a post once at most. Further, although we strove to identify the narratives from the points of view of the posters, some subjective labels might have been affected by the opinions of the authors (see Positionality Statement below). We hope sharing this data will allow for reproducibility and an easier re-examination of the narrative extraction process.

As the content analyzed here was produced during wartime, special care must be taken in an ethical analysis of the potentially sensitive material it contains, despite being published by verified accounts. For instance, @DefenceU posts a variety of reporting from the battlefields, including active war situations involving the use of artillery, images of combatants, and distressing images of affected civilians. We assume that the posting accounts have obtained sufficient permissions to post images of individuals, and have performed intelligence analysis to make sure no important information is being disclosed. Nonetheless, in this manuscript, we endeavored not to use any specific names or locations. Further, by its very nature, this data contains images of and information on vulnerable populations (including children), some of which are affected by the conflict. Special care must be taken that their images and other contexts are not misused and that any harm is minimized in subsequent analyses.

Positionality Statement

All authors reside in “the West”, and one was born in Russia. Although it is our aim to conduct the analysis as objectively as possible, this positionality is likely to affect some interpretation of the results. We make the data and annotations available for the examination of the research community, and possible alternative interpretations.


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The first data collection was performed using the Twitter Streaming API using keywords determined using snowball sampling during the first week of the invasion. The keyword “Ukraine” was translated into some of the most used languages and alphabets, as can be seen in Figure A.1.


(1) Number of panels.

• Single panel: images that are composed of only one image

• Multiple panels: images/posts that are composed of a series of images

(2) Type of image.

• Photo: a picture taken by a camera

• Screenshot: an image of a screenshot taken from a computer screen

• Illustration: a drawing, painting, or printed work of art

(3) Scale.

• Close-up: a shot that tightly frames a person or object, such as the subject’s face taking up the whole frame

• Medium shot: a shot that shows equality between subjects and background, such as when the shot is “cutting the person in half”

• Long shot: a shot where the subject is no longer identifiable and the focus is on the larges scene rather than on one subject

• Other: there is no clear subject or background

(4) Type of subject.

• Object: refers to a material thing that can be seen and touched, like a table, a bottle, a building, or even a celestial body

• Character: refers to people or anthropomorphized creatures/objects, such as cartoon characters

• Scene: when the situation or activity depicted in an image is its main focus, instead of it being on the single characters or objects depicted in it

• Creature: refers to an animal that is not anthropomorphized

• Text: text is the focus of the image.

• Other: anything else, e.g. posters or infographics

(5) Attributes of the subject. For images whose subject is one or more characters, we consider whether the image’s visual attraction lies with the character’s facial expression or with their posture. For the other attributes, we identify five features.

• Facial expression: only for a character

• Posture: only for a character

• Poster: informative large scale image including both textual and graphic elements. There are also posters only with either of these two elements. Posters are generally designed to be displayed at a long distance. It informs or instructs the viewer through text, symbols, graph, or a combination of these.

(6) Contains words. Whether words are a part of the image, including superimposed on top of an existing image.

(7) Character face/posture emotion.

• Positive, Negative, or Neutral: only for a character annotated as facial expression

(8) Narrative (possible more than one).

• Hero (benevolent, strong)

• Victim (benevolent, weak)

• Villain (malevolent, strong)

• Fool (malevolent, weak)

• Other

• None

(9) Emotional appeal. [Open label]

• Humor: satirial, sarcastic, or funny

• Fear: threats, harms, or calamities

• Outrage: scandals, corruption, or moral hazards

• Pride: objects/scenes of adoration or affection

• Compassion: sympathy and sorrow for another who is stricken by misfortune

• None

(10) Intent. [Open label]

• Informational

• Call to action

• Other

(11) Actor. [Open label]


Table C.3 shows inter-annotator agreement between the authors for annotation of the data described in Methods. In aggregate, we find the labelers to be mostly in agreement, and the least of all for the open label of Actor (which was later cleaned and aggregated).


Figure D.2 contains summary statistics of content visual features. The content produced by the three accounts has markedly different features. The @uamemesforces tends to publish content spanning both multiple and single panels,

have mostly a medium scale, and have a character present, for which both posture and facial expression may be important, and which vast majority of the time contains words. @Ukraine and @DefenceU accounts are more similar to each other, in the way that most of their content has a single panel, it uses more illustrations/posters, which contain text. @DefenceU is especially more likely to have content without words and with neutral emotive expressions.

This paper is available on arxiv under CC 4.0 license.