M U LT I M E D I A I N
D I G I T A L D I P L O M A C Y :
Bas Roos 1467344 Bachelor thesis Public responses to digital diplomacy Rebekah Tromble 12 June 2017 name studentnumber subject instructor A C A S E S T U D Y O F T H E I R A N N U C L E A R D E A L A N D T H E T T I P
Introduction
The practice of public diplomacy is increasingly related to new digital possibilities. Especially social-networking services can be considered as tools for diplomatic activities that enable public persons to ‘amplify traditional diplomacy efforts’ (Lichtenstein, 2010). In this light, a recent trend is evident from scholars in IR and public diplomacy who decided to turn their attention to social media engagement with foreign publics (Duncombe, 2017, p. 550). Today, ministries of foreign affairs and diplomats are actively using Twitter and Facebook in order to engage with their audiences (Kampf, Manor & Segev, 2015, p. 332). This so-called digital diplomacy brings along some major advantages for both authorities and citizens. On the one hand, governments can easily make statements or react to certain events using social media services. On the other hand, a wide range of these platforms enables citizens to react on political decisions and engage directly in a dialogue with governments (Bjola & Jiang, 2015, p. 2). Twitter is probably the most relevant tool for diplomats in this context, as it stands out for its ability to “frame representations of state identity” (Duncombe, 2017, p. 551). It is, thus, not without reason that Twitter can be considered as the number one social media tool among representatives. “According to the Twiplomacy website, there are now 228 MFAs and foreign ministers active on Twitter, in addition to some 400 heads of state and more than 200 missions to UN Institutions” (Kampf, Manor & Segev, 2015, p. 332). All these actors have different ways of shaping their public messages.
This paper aims to address the effectiveness of using multimedia on Twitter for public diplomatic purposes by examining the cases of the Iran nuclear deal and the Transatlantic Trade and Investment Partnership (TTIP). More specifically, the following research question will be the point of focus of this study: does the use of multimedia in tweets increase public engagement
in diplomatic topics? In the first place, the overall idea is that the effectiveness of Twitter in
diplomacy campaigns should primarily be understood on the basis of whether tweets create public engagement among the audience. An understanding of which type of tweet creates the most public engagement will shed light on how diplomats should construct tweets in such a way that the message serves its goal. Digital diplomacy is without doubt an integral part of current world politics and knowing how diplomats can best reach their audience is vital in an era in which there is an abundance of information. In the second place, a significant amount of research on different types of messages has been conducted in the field of the psychology of communication, marketing and social media. Given the fact that the features of these disciplines are so prominent in public diplomacy today, this should also be an area of interest for research
in the field of international relations. With regards to public diplomacy, however, no study to date has focused on the specific relationship between the types of tweets on the one hand and the level of public engagement they create on the other.
In the following section, I first discuss the theoretical and empirical literature in the light of my research question in order to lead up to a hypothesis. A review of articles in the field of the psychology of communication, advertising strategies and social media resulted in a hypothesis which addresses the difference in creating public engagement between multimedia tweets and textual tweets. Second, I will justify the methodological choices and explain the data collection process in the section of my research design. Third, the results of the statistical test will be presented. In both cases, two Independent Samples T-Tests showed that multimedia tweets generated significantly more activity than textual tweets. Besides, a simple regression analysis demonstrated the relevance of the type of tweet in creating public engagement. Finally, these findings will be discussed in the light of my research objective in the concluding section of this study.
Literature review
Background and relevance
Since the 1990s, a gradual shift can be identified from traditional diplomacy, i.e. a government-to-government interaction, towards public-oriented diplomacy. The latter has been defined as “the way in which both government and private individuals and groups influence directly or indirectly those public attitudes and opinions which bear directly on another government’s foreign policy decisions” (Signitzer & Coombs, 1992, p. 138). Thus, the core objective of public diplomacy is shaping public attitudes and opinions so that they are in line with a state’s self-interest. This makes public diplomacy an important soft power tool. After all, it is an agenda-setting instrument which can encourage the target audience to adopt corresponding ideas and to support a country’s foreign policy (Nye, 2008, pp. 94-95).
With the emergence of new technologies such as social media, a new form of diplomacy became relevant in spreading soft power. This so-called digital diplomacy is a broad concept that refers to the impact of all kinds of information and communication technologies (ICTs) on diplomatic practices (Manor, 2016, p. 3). A variety of studies have researched for example the potential of digital diplomacy (Manor, 2016), its relevance and effectiveness in the field of public diplomacy engagement (Bjola & Jiang, 2015) and the use of it by a specific country
(Zhong & Lu, 2013). Twitter seems to be the prominent social media tool in international politics. The service is called “another platform of dialogue between states”, on which “diplomats are increasingly relying on […] in their daily practice to communicate with their counterparts” (Duncombe, 2017, p. 547). Moreover, it has proved to be useful for influencing events in the long term. Being different from other social media services in terms of the focus on news, real-time events and the use of hashtags, Twitter has played an important role in the almost two-month-long aftermath of Iran’s elections in 2009 (Burns & Eltham, 2009; Nye, 2010). Besides, Barack Obama’s 2008 election campaign, covering a period of nine months, was also greatly supported by the micro-blogging website (Dale, 2009, p. 9). Both examples are other types of political events than what I am interested in. However, the core similarity between these examples and digital diplomacy campaigns is that they are both about persuading the audience using Twitter. This provides a reason to believe that Twitter is widely considered as the most useful tool for digital diplomacy practices today.
Yet, the use of Twitter for digital diplomatic purposes is still not well studied. More specifically, there is a missing link between the types of tweets on the one hand and the different nature of impact that these types of tweets have in terms of public engagement on the other. It is important to address this gap, because it is likely that the effectiveness of Twitter depends on whether tweets are shaped in a way in which it appeals to the people’s imagination. Understanding how tweets can be categorized and which categories of tweets create the most public engagement will shed light on how governments should construct tweets in such a way that the message serves its goal. For this reason, it is appropriate to review literature on public engagement created by Twitter in the world of diplomacy and the public perception on communication strategies.
Determining public engagement
Determining the effectiveness of the use of Twitter as a diplomatic tool is open to different interpretations. One can look for example at research in the field of viral marketing, in which is argued that “giving the right message to the right messengers in the right environment” is essential in order to have a message that “leads to a high reproduction rate” (Kaplan & Haenlein, 2011, p. 257). The higher this reproduction rate, the more new people receive the message. In the context of Twitter, this would mean that a tweet which meets the requirements above is more likely to be retweeted and therefore transferred to new people. For this object of study, however, I am interested in effectiveness measured in terms of public engagement. After all, as
outlined above, the core objective of public diplomacy, and therefore digital diplomacy, is influencing public attitudes and opinions. Shaping the global public opinion is increasingly relevant for states acting on the highest diplomatic level (The Foreign Policy Centre, 2014). In order to exert influence on its public, it is likely that governments need to create public engagement in issues that are of importance for their foreign interests. Unfortunately, little research demonstrates the success of social media, let alone Twitter, in assisting governments to do so.
However, Bjola & Jang focused on the impact of social media and provided a heuristic framework which can be used for determining the success of social media for public diplomatic purposes. They argued that setting the agenda, expanding presence and generating conversation are the three essential aspects of engagement in public diplomacy. According to the authors, “each dimension speaks to an important aspect of exerting influence: message content, informational reach, and mode of engagement with the audience” (Bjola & Jang, 2015, p. 3). For the current analysis, message content is not relevant as the topics posted about are limited to the Iran nuclear deal and the TTIP. Therefore, public engagement can be understood on the basis of 1) the informational reach and 2) the mode of engagement with the audience.
Public perception on communication strategies
Online messages can take different forms of communication. The most general distinction is one between visual-based communication and language-based communication. Just like words, visuals are essential for communicating information. Indeed, visuals can be more useful in spreading a message than language. Visuals ‘speak’ directly to our senses and “constitute imagination more affectively. More specifically, “photos supersede the mimetic dimension of reality (“how it really was”) by its aesthetic quality and thereby exercise power over the event” (Heck & Slag, 2009, p. 3). However, what is the theoretical logic that underlines this reasoning? In order to answer that question, one has to examine the psychological understanding of the role of visuals in comprehending information. How, exactly, does one’s perception of visual-based communication differ from that of language-based communication? The overall idea is that people can link a visual image to a picture in their head. This means that different components of a message are always understood in their context (i.e. in relation to each other) when this message is presented in the form of visuals. For example, “the verbal descriptions of a person as having brown eyes, blonde hair, a big nose and a mustache can be conceptualized independently of one another. However, the visual image that is formed on the basis of this
information conveys the features in a specified spatial relationship to one another” (Wyer, Hung & Jiang, 2014, p. 245). Thus, it is likely that events, opinions, news items etc. are understood at least quicker and more completely when they are put forward in the form of visuals instead of words. After all, “the cognitive processing of visual images is similar to the processing of information that is acquired through direct experience” (Wyer, Hung & Jiang, 2014, p. 245). This advantage of using visual content in messages is supported by several disciplines.
First, in the case of advertising strategies, it is known that visual content is better at influencing recognition and that it has a higher ability to recall certain messages than words (Rossiter & Percy, 1980, p. 10). For example, images can elicit emotions by showing a real person or object. Besides, images are sometimes proof that something has happened (Messaris, 1997, vii). Another study on the role of visual imagery in framing a message illustrates that even an implicit use of visual content influences the way in which an individual handles a message (Abraham & Appiah, 2006, p. 183). This phenomenon is explained as implicit visual propositioning, which refers to “the use of visual images (with implied information beyond that stated explicitly in the verbal text) juxtaposed with the explicit verbal statements to make a comment, proposition or suggest new meanings that go beyond the meanings simply produced through the written or verbal narrative” (Abraham & Appiah, 2006, p. 185). To put it in simple terms, this concept is about visual images changing the meaning of text which would have been understood differently when presented on its own.
Second, studies in social media also suggest that visuals can be more effective in spreading a message than language. Visual content is preferable to texts in terms of memorizing and processing information. Besides, images are of greater benefit for activating people’s emotions than merely texts (Pang & Law, 2017, p. 56). Both statements can be explained by the theoretical reasoning above, which is about the fact that people link visuals to pictures in their head. After all, it is likely that memorizing or processing information and bringing up emotions is stimulated by the presence of a visual understanding in people’s minds. Research focused more specifically on Twitter illustrates that there is empirical evidence that tweets containing visuals are more likely to be retweeted than textual tweets (Pang & Law, 2017, p. 56). This is in line with the changes Twitter made regarding its interface over the last couple of years. In general, Twitter increased the focus on images and videos by clearly displaying them on profiles, presenting them directly in the Twitter feed and launching a video application called Vine. All of these innovations point directly to the fact that Twitter itself is also aware of the power that visuals have in driving engagement (Neher, 2014, p. 105).
In short, it is expected that the effectiveness of using Twitter for diplomatic purposes depends on whether it creates public engagement. Besides, existing research in the field of the psychology of communication, advertising strategies and social media emphasizes the benefits of using visual content for spreading certain messages. Just as it is the case for public diplomacy, the major goal of marketing and social media campaigns is to persuade a particular target group. Given this similarity in objective between these disciplines and public diplomacy, it is likely that the theoretical logic underlining this reasoning also applies for the use of Twitter for digital diplomatic purposes. Taken this together and using a twofold content-based categorization of tweets, one might expect the following:
H1: Multimedia tweets are more likely to create public engagement in diplomatic topics than textual tweets.1
Methodology
Methodological justification
The first task was to come up with a research design which is best suited to my research objective. In the first place, I am dealing with an explanatory research question. A case study is in this light an appropriate choice, as it stands out for its ability to provide answers to ‘How?’ and ‘Why?’ questions (Rowley, 2002, p. 16). In the second place, I am interested in a contemporary phenomenon within its own context. In the third place, I have no control of this phenomenon. More concretely, I have no influence on the performance of a Twitter account. These three considerations, the first being the most important one, make a case study the best design for the current study. As summarized by Yin, a case study is an appropriate choice when “a how or why question is being asked about a contemporary set of events over which the investigator has little or no control” (Rowley, 2002, p. 17).
In selecting cases, there was one primary factor to consider: what are the most important topics on the international political agenda? Answering this question from a realist perspective, security issues stand out as ‘high politics’ and therefore as the most vital issues in the international arena (Ripsman, 2000, p. 1). However, considering the 2008 financial crisis and
1 Multimedia is here defined as content which contains both text and visuals. For further explanation, see the
the interconnectedness between security and economy, it is arguable that economic issues also belong on the top of the agenda. As explained by Ripsman, a distinction made between security topics and economic topics overstates “the degree to which economic considerations and national security were ever separable”. For example, one can think of economics as the indispensable foundation of military power and national security (Ripsman, 2005, p. 1). Therefore, I decided to select one Twitter account in the field of international security and one in the field of international economy.
The first case is the official Obama Administration account regarding the Iran deal (@TheIranDeal). From both a public and digital diplomacy perspective, the Iran nuclear deal can be considered as an essential long-term event. To begin with, the salience of this case relies on the fact that it is the first major weapon deal in the digital age in which Twitter has played a major role. The micro-blogging website seems to have never been of so much benefit for a multilateral security deal, in which different actors aimed to persuade the public. Indeed, there have been more security deals in the recent past. One can think of the New Strategic Arms Reduction Treaty between Russia and the United States or the gas deal between the European Union and Israel. However, the actors involved in these cases did not use Twitter as a primary tool to express their views. In the case of the nuclear deal, the United States clearly intended to regularly inform the public by setting up an official government account and therefore allowed Twitter a central role in the negotiations. Probably more important is the fact that the Iran deal can be considered not only as a groundbreaking security deal, but also as the beginning of further cooperation between two former enemies. As outlined by Duncombe, “a ‘relatively new’ but ‘extraordinarily important’ situation is arguably the result of a relationship built through both personal interaction and sustained Twitter communication during the P5+1 nuclear negotiations between 2013 and 2015” (2017, p. 546). This nuclear deal might, therefore, lead to a major shift in international politics in the long term. The United States Twitter campaign regarding the Iran nuclear deal is in that line a striking example of digital diplomacy at the highest level.
The second case is the official account of the International Trade Department of the European Union (EU) that focusses on the TTIP (@EU_TTIP_team). This case represents the other prominent topic in the world of public diplomacy. In contrast to the non-proliferation talks between the United States and Iran, the TTIP negotiations have not led to a final agreement yet. Nevertheless, the Twitter account is still actively spreading information about the progress of the deal. To begin with, the salience of this case relies on the fact that it is not just another trade negotiation. It embodies a process that goes beyond only economic considerations. It seems to
be a deal that “has found itself at the centre of controversy, and the way the debate is going tells us a great deal about politics in our ‘post-democratic’ societies”. More specifically, a struggle can be identified between the population and the business elite on the one hand. On the other hand, these elites face resistance form civil society groups (Crouch, 2014, p. 176). This makes it an excellent case for the current study, as it can be expected that a lot of controversy results in an important role for Twitter as a channel through which information, statements and emotions can be spread. Besides, Twitter is likely to have fulfilled an active role during the negotiations. As argued by Richter, “media coverage of the planned Transatlantic Trade and Investment Partnership between the EU and the US has contributed to a critical debate on the topic. In doing so, difficulties have arisen in differentiating factually substantiated arguments from one-sided statements” (2014, p. 1). In other words, media have been the driving force in the debate and given the fact that nearly all (traditional) media platforms are active on Twitter, it is easy to see the essence of the micro-blogging website in this context. These factors combined make the TTIP account an excellent case for our research objective.
This objective is to test whether theoretical reasoning regarding visuals in communication coming from several disciplines also applies to the use of Twitter in digital diplomacy. More broadly, this study aims to test the hypothesis in another sphere, i.e. in the field of social media and international relations. Given what we know about the use of visual content in communicating information, it is expected that the data from these cases supports the hypothesis. No research to date has examined the theoretical reasoning underlining visual content in messaging in relation to the use of Twitter for digital diplomatic purposes. In that sense, this study can provide a better understanding of the relationship between types of tweets and public engagement in the field of digital diplomacy.
Categorization of tweets
When aiming to determine the amount of public engagement that Twitter creates in diplomacy campaigns, it is useful to distinguish between different categories of tweets. Such content classification enables one to identify the characteristics of a tweet that create the most public engagement with regards to a specific topic. Several academics have published articles in the field of the classification of tweets. The majority conducted a sentiment analysis of Twitter data (Pak & Paroubek, 2010; Kouloumpis et al, 2011; Agarwal et al, 2011). Yet, in the light of the theory section above, we are primarily interested in the type of the message content, not in the
categorization of tweets. Despite the fact that their research objective was to analyze sentiments too, they made a simple distinction between textual tweets and multimedia tweets which is also suitable for this research (p. 46). While they primarily focused on social images, the current research takes all kinds of multimedia into account (i.e. text in combination with images, videos, GIFs or links to external sources that are visually presented). This results in a twofold categorization of tweets:
§ Multimedia tweets § Textual tweets
Independent variables
The independent variable is a single dichotomous variable, namely the type of tweet. This variable can be divided into the two categories:
§ Multimedia tweets § Textual tweets
Dependent variables
There are three continuous outcome variables: § Dependent 1: replies
§ Dependent 2: retweets § Dependent 3: likes
As outlined in the literature review, public engagement can be divided into two dimensions for this study: 1) informational reach and 2) conversation gathering with the audience. In the case of informational reach, one should look at replies, retweets as well as likes. In order to determine the level of conversation gathering with the audience, it is sufficient to look at the amount of replies that a tweet generates.
First, the informational reach can be determined by the number of replies, which shows the amount of people who replied on a tweet after it showed up. Besides, retweeting is a way to share other tweets with your own followers. This action can imply different things.
According to Twitter itself, it is a way to show that you agree with the message or that you find the topic interesting. Yet, many people mention that ‘a retweet is not endorsement’. This makes it an ambivalent action which is difficult to define, as it seems to be a way to both endorse a message and to do the exact opposite. Thus, more generally, the number of retweets indicates how many people have been triggered to share a tweet with their followers after it showed up. Finally, likes indicate the amount of people that want to show their appreciation for a tweet after it showed up. Second, in order to determine the level of conversation gathering, one should only focus on the number of replies. After all, replying to a tweet means that people actively react on the content by returning a message. This will always result in a conversation including at least two tweets (the original tweet and the reply). In contrast, retweets and likes do illustrate a part of the activity that has been generated, but cannot be considered as tools for starting a conversation. Although quote tweets have the same conversational effect as replies, these do not fit in this research design as they do not show variance in terms of my independent variable. After all, Twitter does not enable its users to add multimedia to a quote tweet.2
Data collection
The data will be collected using a Google Chrome extension called Web Scraper. This tool enables one to scrape all kinds of data from Twitter accounts, for example dates, tweet texts and retweets. The current study focusses on replies, retweets and likes as the three indicators of public engagement. Retweets are excluded from the analysis in both cases, as this makes me sure that any variance among the dependent variables can at least not be explained by different numbers of followers. Regardless of the content, it is likely that retweets from accounts with many followers generate more activity than retweets from accounts with less followers. I can confidently assume that the exclusion of retweets has a positive effect on the internal validity of this study.
Time frame
In the first place, ‘Adoption Day’ (October 18, 2015) was the day that the Iranian authorities approved the nuclear agreement. More specifically, it was the day that the deal took effect. Yet, the negotiations between the United States and Iran started years before. Since 2006, the United
2
States has negotiated with Iran about nuclear non-proliferation (Katzmann & Kerr, 2016, summary). However, the United States set up an official Twitter-account in July 2015 and actively spread its message until July 2016. This period of a year does cover the most critical moment in the deal, i.e. the Adoption Day. Since our expectations about visual content in messages are not time-bound, it is appropriate to collect the data over all tweets sent by the account.
In the second place, it is appropriate to come up with a comparable time frame for the second case. The time frame for the analysis of the TTIP account also needs to be centered around a crucial event or period. Unfortunately, there is no ‘Adoption Day’ in the case of the TTIP yet. However, there are more ways to construct a suitable time frame. The EU started the negotiations on June 14, 2013 (Culture Action Europe, 2016, p. 1). From this moment on, there have been several notable moments. In 2014, the EU aimed to enhance its transparency regarding the trade deal. For example, it was decided to start publishing reports of the negotiations. However, the measures did not prevent a series of leaks throughout 2016. A growth of concern can be identified when organizations like Greenpeace started to leak chapters of agreements and other papers (Culture Action Europe, 2016, p. 3). This makes it an interesting period for the current research, as it is likely that these leaks caused some friction among the public which may have been expressed on Twitter. Besides, France pledged to call for an end of the negotiations due to a lack of progress in the same year (Culture Action Europe, 2016, p. 1). This can also be expected to have caused some sort of controversy. Therefore, data will be collected from January 2016 up to and including December 2016. This time frame covers a critical period and has the same length as the time frame that is selected for the other account.
Analyzing multimedia tweets
The data collection has been divided into two phases. First, multimedia tweets have been analyzed using Web Scraper. I selected three different types of multimedia tweets:
§ Tweets including text and an image. § Tweets including text and a video.
§ Tweets including text and a link to an external source which is visually presented. This means that tweets including links to external sources which do not show the content of that source (i.e. links that are just presented as an URL), are not part of the multimedia
tweets. This is in line with the research objective, because an URL is not a visual representation.
A total number of 383 multimedia tweets have been scraped from @TheIranDeal and exported to an Excel data sheet. A total number of 135 multimedia tweets have been scraped from @EU_TTIP_team and exported to an Excel data sheet.
Analyzing textual tweets
The second phase has been concerned with collecting data from textual tweets. Unfortunately, there is no tool available for analyzing textual tweets apart from multimedia tweets. Therefore, this second analysis has been conducted manually. All tweets that I did not consider as multimedia tweets are considered as textual tweets in this second collection stage:
§ Tweets including exclusively text.
§ Tweets including exclusively text and/or an URL which is not visually presented. § Tweets that link to another Twitter account or tweet (also known as quote tweets). These
do not include links to external sources, as the source is still Twitter.
A total number of 336 textual tweets have been collected from @TheIranDeal and ordered in an Excel data sheet. These complemented the multimedia tweets and make a total number of 719 tweets, which covers all original tweets sent by the account. A total number of 939 textual tweets have been collected from @EU_TTIP_team and ordered in an Excel data sheet. These complemented the multimedia tweets and make a total number of 1074 tweets, which covers all original tweets sent by the account in 2016. The two separate data sets are for both cases merged into a SPSS file consisting of the three dependent variables and the single dichotomous independent variable.
Main methodology to test the hypothesis
In the first place, the main statistical test for this research will be an Independent Samples T-Test. The overall objective of this analysis is to look for an effect of the two categories of the independent variable on the three dependent variables. This analysis enables one to relate these two categories, namely multimedia tweets and textual tweets, to the number of replies, retweets
and likes. I perceive the three dependent variables as three different forms of public engagement on Twitter, instead of treating them as separate elements which define public engagement only when they are considered jointly. As explained above, each of the outcome variables represents a distinct form of public engagement. For example, one can speak of a certain level of public engagement even if a tweet only generated retweets. Analyzing replies, retweets and likes separately enables one to identify possible differences between multimedia tweets and textual tweets, which is interesting to look at.
In the second place, an Ordinary Least Squares (OLS) regression will be conducted in order to gauge how much of the variation in the three dependent variables is explained by the type of tweet. For this purpose, the categorical predictor variable is recoded into a dummy variable. The OLS regression will show the importance of the type of tweet as an explaining factor in relation to other possible factors. This issue will be further addressed in the discussion. Results
@TheIranDeal
First, the descriptive statistics in table 1 show the average number of replies, retweets and likes for both multimedia tweets and textual tweets sent from @TheIranDeal. The analysis shows that multimedia tweets achieve a higher average score in terms of replies, retweets and likes. Table 1.
Descriptive statistics of the independent variables
Type of tweet N Mean Std. deviation
Replies Textual 336 8.98 9.252 Multimedia 383 19.35 38.113 Retweets Textual 336 35.82 32.197 Multimedia 383 79.19 117.350 Likes Textual 336 26.17 29.728 Multimedia 383 60.40 119.203
Second, it is appropriate to present the actual T-Test results. The findings in the table below demonstrate that multimedia tweets and textual tweets statistically differ on replies, retweets and likes considered separately.
Table 2.
Independent Samples T-Test
t df Sig. (2-tailed)
Replies -4.861 717 .000
Retweets -6.564 723 .000
Likes -5.127 723 .000
Based on these data, it appears that multimedia tweets create more public engagement than textual tweets. The statistical results lead up to the conclusion that H1 is supported by the data from the Twitter account regarding the Iran nuclear deal.
Finally, I calculated a R² value for each dependent variable by doing an OLS regression. For replies, a value of .032 has been calculated, which means that 3,2% of the variation in replies is explained by the type of tweet. 5,6% of the variation in retweets and 3,5% of the variation in likes is explained by the type of tweet (see table 3).
Table 3. Model summary R R Square Replies .179 .032 Retweets -.237 .056 Likes .187 .035 @EU_TTIP_team
First, the descriptive statistics in table 4 show the average number of replies, retweets and likes for both multimedia tweets and textual tweets sent from @EU_TTIP_team. It appears, again, that multimedia tweets achieve a higher mean than textual tweets in terms of all three dependent variables.
Table 4.
Descriptive statistics of independent variables
Type of tweet N Mean Std. deviation
Replies Textual 939 1.45 2.080 Multimedia 135 4.81 13.675 Retweets Textual 939 4.41 8.142 Multimedia 135 33.30 78.685 Likes Textual 939 4.10 5.837 Multimedia 135 20.44 37.742
Second, it is necessary to present the univariate results from the T-Test. The findings in the table below demonstrate that multimedia tweets and textual tweets are significantly different in terms replies, retweets and likes. Thus, the results showing a higher average score for multimedia tweets are statistically significant.
Table 5.
Independent Samples T-Test
t df Sig. (2-tailed) Replies -7.005 1072 .000
Retweets -10.884 1072 .000
Likes -12.314 1071 .000
Based on this statistical test, I can conclude that H1 is also supported by the data from the Twitter account regarding the TTIP.
Finally, I calculated a R² value for each dependent variable for this case too. For replies, a value of .044 has been calculated, which means that 4,4% of the variation in ‘replies’ is explained by the type of tweet. Table 6 shows that for retweets and likes, respectively 10% and 12,4% is explained by the type of tweet.
Table 6. Model summary R R Square Replies .209 .044 Retweets .315 .100 Likes .352 .124 Discussion
This study examined whether the use of multimedia in tweets increases public engagement in diplomatic topics. First, existing literature in the field of public diplomacy and the effectiveness of using Twitter for diplomatic purposes has been reviewed. It is argued that one should look at the level of public engagement in order to assess the effectiveness of Twitter. This can be understood on the basis of both informational reach and the level of conversation gathering with the audience. Furthermore, studies on the psychology of communication, advertising strategies and social media are reviewed in order to lead up to a hypothesis. I have tested the hypothesis that multimedia tweets create more public engagement in diplomatic topics than textual tweets. Second, a case study including two cases has been conducted to test this expectation. The official Obama Administration account regarding the Iran deal and the official account of the International Trade Department of the EU focusing on the TTIP are selected for this purpose. The results of this case study are in line with the expectation that the use of multimedia in tweets increases public engagement in diplomatic topics.
Interpretation of the results
To begin with, T-Tests for both cases showed that there is a statistically significant difference between multimedia tweets and textual tweets in terms of the number of replies, retweets and likes. It appears that multimedia tweets have a higher average score for these three dependent variables than textual tweets. For both cases, multimedia tweets have the highest means for all dependent variables with a significance level of .000. Thus, there seem to be no substantive differences between replies, retweets and likes based on the analysis of these cases, which means that the type of tweet has statistically the same effect on the informational reach and the level of conversation gathering with the audience. However, it is interesting to note that the TTIP account produces a bigger difference in mean between multimedia tweets and textual
tweets than the account regarding the Iran nuclear deal (compare table 1 and 4). This could have to do with the difference in topic posted about or a different composition of followers. However, research focused more directly on this issue is needed in order to assess the role of these circumstantial factors. All in all, based on this case study, it seems that the assumption that visual messaging is more attractive to people than textual communication maintains in this context of Twitter diplomacy. In that sense, this study enriched the existing knowledge in the field of public perception on communication strategies by offering some insights in the dynamics of Twitter and public diplomacy.
Next, in order to come up with an appropriate answer to my research question, the results of the regression analysis should be discussed. The fact that there is a positive correlation between the use of multimedia in tweets and the amount of engagement that these tweets create, does not say anything about the overall role of Twitter content in creating public engagement. The OLS regression demonstrates that the share of the content of a tweet in explaining the variance in my dependent variables is marginal. For replies, retweets and likes of the first case, values of respectively .032, .056 and .035 have been calculated for R² (see table 3). This means that only 3,2% of the variation in replies, 5,6% of the variation in retweets and 3,5% of the variation in likes is explained by the type of tweet. For the second case, this is respectively 4,4%, 10% and 12,4% (see table 6). Despite the fact that these percentages are more or less acceptable in social sciences, they inherently suggest that more factors are affecting the number of replies, retweets and likes of a tweet. Yet, although the proportion of variance in public engagement that is predictable from the type of tweet is not overwhelmingly high, whether multimedia is included or not does play a role for the level of public engagement.
As this is a case study, the findings above cannot be generalized to all active Twitter accounts in the sphere of public diplomacy. The major goal I have achieved here is to open a door to further research in the field of using Twitter for diplomatic practices. The supported theoretical framework can serve as a ‘vehicle’ to generalize to new cases which fit the domain of this theoretical framework. It is now, given these R² values, probably interesting for diplomats to know if there are other factors that have a greater influence on public engagement in their Twitter campaigns. Though, it should be remarked that it was not in my interest to examine this issue. After all, I was primarily focused on the difference in generating activity between multimedia tweets and textual tweets, not on which factors the amount of engagement mostly depends on. This makes the study a stepping stone to research on Twitter and public engagement in more general terms.
Sources of uncertainties
In like almost any other research, there are some sources of uncertainties embedded in the research design. In the first place, tweets sent in the first period after setting up the account are less likely to generate activity than tweets sent at a later stage, because every account needs to build its followers base from scratch. In the second place, it is very important to consider the context in which a tweet has been sent. The moment of sending a tweet is often crucial for the activity that it generates. This assumption is outlined by Kaplan and Haenlein in the context of viral marketing (2011, p. 257). They argued that “some plain old good luck is needed to glue everything together, as it’s often just not the right time and/or place to launch a viral marketing campaign”. It is likely that the same is the case for Twitter campaigns. For example, major news events can distract (or attract) people from the topic for a while and therefore result in less (or more) involvement of the public. In this line, one can think of plenty of other circumstantial factors that can be expected to influence public engagement. It has appeared that considerably more factors affect public engagement and these two sources of uncertainties are therefore worth studying in the future.
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Appendix
Codebook
Dependent variables
§ Replies (value 0) – The number of people that replied to a tweet which shows: o The informational reach.
o The level of conversation gathering with the audience.
§ Retweets (value 1) – The number of people that shared a tweet with their own followers which shows:
o The informational reach.
§ Likes (value -1) – The number of people that appreciated a tweet which shows: o The informational reach.
Taken together, these three dependent variables determine the public engagement that a tweet creates.
Independent variable
§ Type – Either “multimedia tweet” or “textual tweet” o Multimedia tweet (value 1)
§ The tweet contains text in combination with an image. § The tweet contains text in combination with a video.
§ The tweet contains text in combination with a link to an external source which is visually presented.
o Textual tweet (value 0)
§ The tweet contains exclusively text.
§ The tweet contains exclusively text and an URL.
§ The tweet links to another Twitter account or tweet (also known as quote tweets). This kind of tweets do not include links to external sources, as the source is still Twitter.
