How to measure Twitter content polarity

The European Commission’s EMPL presence on Twitter in 2012: Positivity index

Scholars advise identifying the emotional value that is carried by the content itself while doing a content analysis. In the context of this research project, I thought that applying and calculating the Losada Line (also called Losada Ratio) would enhance the results and provide additional insights on communication patterns developed by the research subjects, namely Social EuropeEURes and Commissioner Andor in 2012.

Figure 1: Positivity index analytics based on Losada Line

Figure 1: Positivity index analytics based on Losada Line

The Losada Line is a visual representation of a scale where the values reflect the positive and negative dimensions of a written or an oral speech. The critical point is 2.9, which resulted according to a complex algorithm developed by Marcial F. Losada. A ratio of 3.0 to 6.0 on the Losada Line is the optimal range that reflects a normal positivity index. The index is calculated by dividing the ratio of positive emotions (posemo) by the ratio of negative emotions (negemo). Both “posemo” and “negemo” are two LIWC output values presented in Table 1 in this article.

The positivity index is as follows: 4,14 for Social Europe, 7,12 for EURes and 4,01 for Commissioner Andor while the average user ratio is 4,32, which is optimal (Figure 1). According to Losada Line algorithm, Social Europe and Commissioner Andor produced content with a normal positivity index while EURes appeared too positive, being placed at above 6.0 (7,12). According to Tausczik and Pennebaker[1] (2010), “assents and positive emotion words measure levels of agreement” (p. 32).

References

[1] Tausczik, Y. R. and Pennebaker J. W. (2010), “The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods”, in Journal of Language and Social Psychology, 29 (I) 24-54, Sage Publications

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Twitter linguistic patterns

The European Commission’s EMPL presence on Twitter in 2012: Linguistic patterns

As mentioned in the previous articles, I used LIWC as one of the software tools to establish the linguistic patterns developed on Twitter by the subjects of this research.

23_linguistic_patterns

LIWC was able to detect about 69% of the dictionary words which were part of the input provided by Social Europe, EURes and Commissioner Andor in 2012. The remaining 31% of words may be part of EU terminology that is uncovered by the LIWC dictionaries.

Only 35 information categories and sub-categories, which are relevant to this research, were selected out of 80 in output. They are: linguistic processes, psychological processes and personal concerns.

Each category is introduced in the next paragraphs.

Linguistic processes

  • Word count: the largest tweet corpus is of Commissioner Andor (17,782 words in 716 tweets), followed by Social Europe (15,843 words in 934 tweets) and EURes (6,837 words in 398 tweets). It appears that Social Europe used a condensed communication style since their average word count per tweet is about 17 while EURes had more than 17 and Commissioner Andor about 25 words per tweet.
  • Dictionary words: 69% user average of words were captured by the program, based on its incorporated dictionaries. The remaining 31% may represent EU terminology, which LIWC dictionaries may not contain as they are based on informal and not on a specialised vocabulary.
  • Total function (or style) words: only 34% of the tweet corpora represent function words. A possible explanation would be that due to the 140 character limit, Twitter users rely more on content words to convey a clear message and keep function words (such as pronouns, articles and prepositions) to a minimum. LIWC distinguishes between content and function words. Content words are the backbone of a message (nouns, verbs, adjectives and adverbs), while function words connect, shape, and organise content words (pronouns, articles, prepositions, auxiliary verbs, conjunctions, negations, quantifiers and common adverbs).

They do not have any meaning by themselves but they allow a psychological insight into how people think, feel and connect with others. They are short and used at very high rates but they are also hard to detect in a conversation flow or written text. Function words also require social skills and social knowledge to be used properly: the speaker assumes the listener is familiar with the communication context. According to Pennebaker[1] (2011, p. 33) “the speaker assumes that the listener knows the context”.

  • Total pronouns: EURes leads with 8% followed by Commissioner Andor (4%) and Social Europe (3%) while the user average is 4%. In terms of personal pronouns use, EURes is in the leading position again (6%) followed by Commissioner Andor (2%) and Social Europe (2%).

Personal pronouns captured my attention. For example, “people who pay a great deal of attention to other people tend to use personal pronouns at high rates” (Pennebaker, 2011, p.291). First person singular pronouns were not used often (less than 1% user average). First person plural pronouns reflect a social connection to a group. I noticed a 2% use for EURes and less than 1% for the two others which may indicate that EURes is more “inclusive” by creating a convivial and cooperative environment. Second person pronouns represented about 3% use for EURes, who often address themselves directly to their followers to engage in a more spontaneous way and get closer to the audience. Since EURes offers practical solutions, their tweets often include questions such as “Are you interested in working in Norway? You can read more about it here”.

  • Verbs: the three account holders have a preference for using the present tense (5% user average) and tend to equally use the past and future tenses (less than 1%). The use of the present tense proves a dynamic communication.

Psychological processes

  • Affective processes: both positive and negative emotions (posemo and negemo) will be introduced in the next article as a Losada line. It is worth noting that, according to LIWC output, Commissioner Andor’s content is placed in both the most positive and most negative categories (see posemo and negemo in Table 1). This could be explained through the use of specific words associated to unemployment, in the context of the crisis. However, the positive dimension is visible in the efforts proposing relevant legislation in order to overcome the crisis.
  • Cognitive processes imply perception, learning, and reasoning to facilitate thinking and remembering. Less than 11% of the content shows a certain degree of cognitive processes, which is visible in the Twitter messages (13% Commissioner Andor, 11% EURes and 9% Social Europe). Commissioner Andor’s tweets often placed a number of quotes from his speeches and some of his reflections on the subject of the policies. The account administrator confirmed that the Commissioner liked to tweet relevant quotes from his speeches, which were enhanced with personal reflections.

It is also important to note the inclusive dimension of the cognitive processes, which is represented by words such as “and”, “with”, or “include” and reflects a high use of 3,2% for all three account holders. This may mean that specific vocabulary covering inclusion policy is well employed in the tweet corpora. The exclusive dimension is minor.

  • Relativity conveys information on motion, space and time. The size of both space and time lexical fields is significant: 7% user average for space and 6% user average for time. It is apparent that European Union countries, regions and cities are well represented in the communications. Time-wise, there are many references to event dates throughout the year 2012.

Personal concerns

Personal concerns cover information on work, achievements, home and money.

  • Work includes information about jobs and careers. With 9% user average, this category is quite remarkable (10% EURes, 9% both Commissioner Andor and Social Europe). It is obvious that this category is well represented as all account holders talk about job opportunities and job-related events.
  • Achievement covers information on earnings, winning and successes and represents 4% user average, which may reflect the efficiency of the policy communication as well as the considerable achievements with the events and guidelines. The peak of 4,2% for the Commissioner may be related to his missions and official visits abroad which were successful in 2012, according to the statements in the tweets.
  • Money represents 2% of the content and covers discussions about poverty, salary rights and others. Commissioner Andor is above the user average, with 3%.

Table 1: Selected LIWC categories

Table 1: Selected LIWC categories

However, even though LIWC offers insights into the language patterns of a user, we should also keep in mind that Twitter style is very different from everyday interaction. For instance, the three account holders may not have used many future tense verbs to reflect a future perspective but they are certainly goal-oriented.

[1] Pennebaker, J.W. (2011), The Secret Life of Pronouns: What our words say about us, New York, Bloomsbury Press, 2011

Communication trending topics on Twitter

The European Commission’s EMPL presence on Twitter in 2012: Trending topics

Identifying the trending topics of the communications managed by the three account holders on Twitter in 2012 started with processing the hashtags’ and extracting their occurrences. Comparing the most used hashtags with word frequency (not hashtags, but ordinary words), word pairs (pairs of words based on their proximity) and semantic network nodes may provide clear indications and relevant elements to identify the trending topics of Twitter communications. Both word pairs and semantic network nodes will be further explained and developed in this article.

Most frequent words with at least 52 occurrences

The list of the most frequent words in the unified tweet corpora of the three account holders, namely Social Europe, EURes and Commissioner Andor in 2012, resulted from a WORDij procedure. The word frequency ranged from 465 to 2. I selected the first ranked 42 words with at least 52 occurrences only as from 52 to the next lowest level was a significant gap. The selected words included most of the hashtags and related words. Usernames, locations and other irrelevant words were discarded. The validated words as well as the discarded words (in grey) are placed in Table 1.

The most frequent word pairs

WORDij also extracts word pairs based on an algorithm that uses the rule of word proximity. The application placed 1934 word pairs in output, but I selected the top 32 word pairs. Their proximity makes sense in the context of the Twitter communications of the three account holders and relates well to the hashtag set and the most frequent words (Table 2).

Table 1: Most frequent words

Table 1: Most frequent words

According to Danowski and Park[1] (2013) the “order of words within pairs” is “maintained so that a pair (word A – word B)” is treated as a distinct entity from (word B – word A) (p.24).

Table 2: Most used word pairs

Table 2: Most used word pairs

Twitter semantic networks

Extracting the semantic network nodes with WORDij provided additional information to be triangulated with the other datasets to ensure that key information is not left out. Given some limitations of LIWC (the other piece of CAQDAS tool, which I used), I wanted there to be in place a “human” filter to ensure research credibility.

I created a set of spring-embedded graphs to check how the semantic entities grow from 5 to 30 nodes. The graphs show the gradual growth from the strongest 5 nodes to the following 5, 10 and 20 which enrich the network.

Figure 1: Semantic network; 5 nodes and 3 minimum link values

Figure 1: Semantic network; 5 nodes and 3 minimum link values

The semantic networks of the three account holders, based on the unified tweet corpora, with 5 and 30 network nodes are presented in Figure 1 and Figure 2.

Figure 2: Semantic network; 30 nodes and 3 minimum link values

Figure 2: Semantic network; 30 nodes and 3 minimum link values

The semantic network nodes were analysed and compared against the hashtags, the most frequent words and word pairs. The relevant nodes which are also hashtags are found in Table 3 (#). The discarded nodes are greyed. All nodes (Table 3) are listed in reverse order to show that the strongest nodes are placed at the bottom, which may symbolise a foundation on which the semantic network is built.

Table 3: Semantic network nodes

Table 3: Semantic network nodes

Hashtags vs. word frequency and word pairs

Hashtagging was somewhat applied inconsistently and the existing hashtags could not provide a clear and complete picture of the communication trending topics managed by the three account holders. Hashtagging was performed more accurately by Social Europe and the Commissioner. Often no hashtag or multiple hashtags were assigned to one event or policy.

For example, European Job Day (#EJD) and European Online Job Day (#EOJD) were also tagged as #Job, #Jobs, #onlinejobday, #onlinejobsday, #EuropeanJobDay, #EuropeanJobDays, #EuropeanJobsDay, #jobfair. This was also the case with the Youth Employment Package (#EmplPackage, #Employment package, #EmploymentPackage and #YouthEmploymentPackage).

There were also two similar hashtags: “#poverty” and “#poverty12” which were assigned to different subjects. The first was associated to the 11th European Meeting of People Experiencing Poverty (Homelessness and Housing Rights in the Context of the Crisis) while the second was associated to the Second Annual Convention of the platform against poverty and social exclusion, which is a long-term future strategy of the European Union.

Correlating the four information sets ensured that key and relevant information is elicited in order to get an accurate picture of the communication trending topics managed by the three account holders on Twitter in 2012.

Based on the information I extracted from the individual and unified tweet corpora, I established a list of words, which I searched for in each individual tweet body.

The trending topics I identified are listed along with the words I checked for by searching in the corpora. While checking each tweet body to place it into the right trending topic, I considered it as only one occurrence even though the tweet body might have had one or more hashtags, one or more word pairs or semantic nodes covering the same topic. The topics are ranked according to the occurrences of the content entities. A content entity is represented by either one of the following: a hashtag, a word pair or a semantic node.

  1. Current EU social policies and programmes (607 content entities) including labour market, social dialogue, social investment, social security, Eurofound, #crisis, market, #social, conference, policy;
  2. European Job Day and European Online Job Day: ( 596 content entities) including #EJD, EJD, #EOJD, EOJD, #job/#jobs, job/jobs, day;
  3. Youth Employment Package: (447 content entities) including #youth, #employment/#unemployment, package;
  4. 2012 European Year for active ageing and solidarity between generations (EY2012): (358 content entities) including #ey2012, year, #active #ageing, #solidarity, #generations, awards;
  5. Jobs for Europe: (252 content entities) including #jobs4europe and jobs4europe;
  6. Poverty Convention: (87 content entities) including #poverty12;
  7. Youth Employment chat hosted by Commissioner Andor: (66 content entities) including #youthempl and chat;
  8. Youth Guarantee: (28 content entities) including #youthguarantee, guarantee.

Trending topics number 3 and 8 are two new political initiatives launched in 2012 and, if approved, would be implemented as EU policies and programmes in the forthcoming years.

Trending topic number 6 covers a major long-term initiative which forms part of the Europe 2020 strategy and is currently implemented in the EU. It is a major political priority to overcome the effects of the crisis, such as poverty and social exclusion. These trending topics have got political weight and represent strategic points for the further development of the EU.

Trending topic number 7 is the Youth Employment chat hosted by Commissioner Andor in which he discussed the Youth Employment Package and Youth Guarantee with young people.

The three tweet corpora were placed into individual monthly files for convenience in searching and finding the time references. The related hashtags, nodes and word pairs were counted according to the topic they belonged to. After manual counting, the figures corresponding to the occurrences were placed in a separate spreadsheet. When this work was completed, I grouped all the entries of the three accounts in a single table, which generated the chart in Figure 3.

Trending topics dynamic throughout 2012

Trending topics dynamic throughout 2012

The eight trending topics cover the core communication content which was planned for distribution via Twitter. The topics were confirmed by the account administrators in the interviews. The topics formed part of the communication priorities of the European Commission in responding to the crisis on the one hand, and on the other they were in line with the Europe 2020 Strategy[2] (the EU’s growth strategy for the current decade). The topics reflect most of the employment, social affairs and inclusion policies, with a special focus on the generic theme of the 2012 European Year for Active Ageing and Solidarity between Generations (EY2012). It is worth noting the complexity of the policy content and the professional skills of the communicators in making accessible such content to different target audiences. The topics, as ranked earlier, illustrate different communication approaches, from traditional content distribution (featuring official texts) to an online dialogue (Twitter chat) hosted by the Commissioner himself. Therefore, Commissioner Andor and his team together with the other operational accounts’ administrators tried to sustain a genuine two-way communication to ensure openness and willingness to listen to people’s voices and needs.

The European Commission’s role is to propose legislation and in this context the creation of the Youth Employment Package was also based on both official and public contributions. The online dialogue (Twitter chat) is just an example of the Commissioner’s willingness to value the input of people.

A common dimension of the trending topics consists of a set of tailored measures to help people overcome the effects of the crisis: increasing social dialogue, ensuring social security, finding a job, helping young people gain employment, relying on top level key policy makers to identify suitable solutions to the crisis etc. Both the Youth Employment Package and Youth Guarantee are being implemented and the effects will be soon visible, according to the Commission’s official reports.

More information to come in the next articles.

Previous articles on the same subject

Tweet me a URL and make your communication richer

Why mentions on Twitter help people communicate

The European Commission’s EMPL presence on Twitter in 2012: Content languages and hashtagging

Why mentions on Twitter help people communicate: The European Commission’s EMPL presence on Twitter in 2012

References

[1] Danowski, J.A., and Park, D.W. (2013), Celebrities in the mass and internet media and social network structures: A comparison with public intellectuals. Manuscript. Chicago, IL: University of Illinois at Chicago

[2] http://ec.europa.eu/europe2020/

Subjects communicated by the European Commission’s EMPL on Twitter in 2012

The European Commission’s EMPL presence on Twitter in 2012: Communication subjects

All tweets published by the three account holders, the subjects of this research project, namely Social Europe, EURes and Commissioner Andor in 2012, were copied onto a separate spreadsheet to prepare the raw data for coding.

Figure 27: Tweet subjects per user and subject average (%)

Figure 1: Tweet subjects per user and subject average (%)

I identified six categories of tweet subjects:

  1. EU Social policies and programmes, which also include all the projects funded by the EU programmes managed by DG Employment:
  • EU employment strategy: Employment package, Youth employment, New Skills for New Jobs
  • Social protection and social inclusion
  • Social partnerships
  • Europe 2020 initiatives: Youth on the Move, Agenda for new skills and jobs, European platform against poverty and social exclusion
  • Working in another EU country
  • Funding opportunities
  • Rights at work.
  1. EY 2012 groups all the events and activities organised within the frame of 2012 the European Year of Active Ageing and Solidarity between Generations 2012:
  • Opening and closing conferences of the EY 2012
  • European and national activities related to the EY 2012
  • European Year Award Ceremony (projects and initiatives in the framework of the EY 2012).
  1. Other events includes all non-EY 2012 events such as conferences, debates, campaigns, EURes events, initiatives, press conferences and both online and offline surveys related to the events:
  • EURes online and offline events (European and national events)
  • Business visits of Commissioner Andor
  • Youth employment chat hosted by Commissioner Andor
  • The Employment Policy Conference “Jobs for Europe”.
  1. Publications contains official survey reports, statistics, studies, news and press releases, speeches and factsheets:
  • Eurofound Quality of Life survey (2012)
  • Eurofound European Working Conditions Survey
  • Press and blog articles
  • Eurobarometer public opinion surveys
  • Eurostat statistics reports
  • EC’s White paper: an Agenda for Adequate, Safe and Sustainable Pensions
  1. Guidelines are the practical aspects of the EU Social policies and programmes: general advice, Human Resources assistance, practice examples and online resources featuring job-hunting tips. They were mainly provided by EURes and its network of advisors in the member countries.
  2. Others includes non-related news to the previous categories, EURes partnership agreements with European and national bodies, job and internship opportunities.

Each tweet body was analysed, coded and placed in the relevant category. If a tweet body focused on more than one subject, it was coded and classified according to the dominant subject. For example, if the tweet covered both EU Social policies and programmes and publications, it was placed in the dominant category and not in both. When coding was completed, the data was sorted to have the items grouped by category and counted accordingly.

The results are presented in Figure 1.

The tweets featured a wide range of subjects from communicating the policies to pan-European and national, regional and local events. The subjects cover almost all communication activities that were planned by the communicators in the year 2012, according to what they stated in the interviews.

It is obvious that the communication actions are somewhat replicated at national, regional and local level. Even though most of the content was available in English, the administrators justify the minor coverage of missing languages through the need of establishing a common communication language between the communicators, stakeholders and the audience.

It is also important to note the concrete communication dimension which is visible in the guidelines: assistance to job hunting, workers’ mobility, social protection and social inclusion, to name a few. The publications play a significant role in disseminating the major aspects of the employment, social affairs and inclusion policies together with official statistics and reports published by other European bodies (Eurofound, Eurostat, Eurobarometer reports).

The top subjects of the unified tweet corpora were as follows: other events (29%), EU social policies and programmes (25%), others (14%), EY2012 and publications (both 13%), and guidelines (7%). The administrators validated the tweets’ subjects but not necessarily in this order.

More information to come in the next articles.

Previous articles on the same subject

Tweet me a URL and make your communication richer

Why mentions on Twitter help people communicate

The European Commission’s EMPL presence on Twitter in 2012: Content languages and hashtagging

Why mentions on Twitter help people communicate: The European Commission’s EMPL presence on Twitter in 2012

 

Measuring Twitter engagement: An example

The European Commission’s EMPL presence on Twitter in 2012: Engagement

Listening to the followers and engaging with them is a key action towards understanding what they say and want. The “engagement rate” concept has recently been introduced to explain and measure interaction of social media/networks users on different platforms. According to the literature, measuring engagement on social media platforms and on Twitter, in particular, raises a number of questions which have not been completely answered to date.

A number of commercial tool owners claim that their algorithms work very well, but their claims have not been yet validated by the research to date. For instance, Socialbakers[1] and Kaushik[2] created formulae that calculate the Tweet engagement rate, based on a number of metrics.

Figure 1: Socialbakers formula

Figure 1: Socialbakers formula

According to the Socialbakers formula (Figure 1) the engagement rate of the three account holders would be 12 for Social Europe, 12 for EURes and 43 for Commissioner Andor. The average rate of the three account holders would be 19 (Table 1).

Table 1: Engagement rate according to Socialbakers formula

Table 1: Engagement rate according to Socialbakers formula

Kaushik proposes another set of formulae, which provide some indicators about amplification, conversation and applause rates (Figure 2).

Figure 2: Kaushik's formulae

Figure 2: Kaushik’s formulae

Amplification indicates the rate a tweet gets retweeted. Conversation represents all the replies to a tweet while Applause designates all favourites (Table 2).

Table 2: Engagement rate according to Kaushik’s formula

Table 2: Engagement rate according to Kaushik’s formula

What both Socialbakers and Kaushik do not provide is a scale/benchmark to enable one comparing the results and therefore making judgements about the engagement rate.

Klout[3] provides a website and mobile application that enables users to get a score, a so called “Klout” score, which is based on a methodology that measures one’s activity on multiple social media and networks platforms. The score range is from 1 to 100. Klout has never disclosed the algorithm used to score people activity on the social media/networks platforms.

The Twitter engagement rate results when looking at four categories of metrics (replies, favourites, RTs and mentions) that are part of the formulae described earlier. The metrics reflect degree interaction that occurred between the account owners and their followers. The outcomes resulting by applying the two formulae cannot be compared to a scale since both Socialbakers and Kaushik do not provide a benchmark. Therefore, as the four metrics categories are part of this research I would suggest a solution that may provide a better picture of the three account holders’ engagement on Twitter. The solution helps answer one of my research sub-questions and it is not intended to be a benchmark, but a logical set of figures that could be discussed in relation to three areas: a) aggregate annual engagement breakdown, b) aggregate engagement country coverage and c) ratio: engagement, annual follower growth and tweet volume.

  1. a) Aggregate engagement breakdown in 2012
Figure 3: Annual aggregate engagement breakdown 2012

Figure 3: Annual aggregate engagement breakdown 2012

The aggregate engagement breakdown in 2012 resulted from summing-up the four metrics categories that indicate the engagement behaviour throughout the entire year 2012 (Figure 3).

The highest density of the engagement activities involving all three account holders together occurred in September 2012 with the highest number of replies, favourites and RTs. The highest number of mentions was recorded in December 2012 during the chat hosted by Commissioner Andor. The lowest density was recorded in July. In terms of the four engagement categories, RTs are in the leading position with the highest constant occurrence throughout 2012, while the replies were insignificant from January to August and they slightly increased from September to December 2012.

  1. b) Aggregate engagement country coverage

This indicates the interaction distribution by country and the intensity of the interaction in 2012, which varies from one EU country to another. Given the inexistence of a benchmark, I considered the full sum of metrics as a 100% engagement reference, to which I compared the percentage by individual country. If the 100%, in theory, would cover the 27 EU member countries (in 2012), I divided 100 to 27 and that makes a 3.7% average engagement by country. I then established a five level Likert[4] scale where I labelled each level and assigned a relevant range percentage to each of them, from 27.3% (the best, recorded by a country) to the poorest, which is 0.0% (Estonia, with 2 interactions).

Figure 4: Aggregate engagement country coverage

Figure 4: Aggregate engagement country coverage

I tried to balance the scale in such a way to make a reasonable categorisation and ranking based on the 3.7% average: outstanding (between 27.3% and 16.1%), good (between 4.6% and 2.1% and the average of 3.7% I consider closer to the level “good”), average (between 1.4% and 1.0%), unremarkable (between 0.8% and 0.2%) and poor (0.1% and 0.0%).

The aggregate engagement country coverage based on this scale is pictured in Figure 4, while the individual country results are available in Table 3.

Table 3: Aggregate engagement (Country coverage)

Table 3: Aggregate engagement (Country coverage)

In Table 3, engagement volume sums up the four engagement parameters: RTs, mentions, replies, and favourites. In terms of engagement country coverage the results are as follows:

1) Outstanding coverage: Belgium, Spain and the UK (so-called “old member countries”)

2) Good coverage: The Netherlands, Italy, France, Ireland, and Greece (so-called “old member countries”)

3) Average coverage: Sweden, Germany, Latvia, Hungary, Portugal, and Romania (combined old and new member countries)

4) Unremarkable coverage: Austria, Poland, Denmark, Luxembourg, Finland, Slovenia, Cyprus, and Bulgaria (combined old and new member countries)

5) Poor coverage: Slovakia, Czech Republic, Malta and Estonia (new member countries).

The statistics exclude countries outside the EU as well as suspended Twitter accounts. There was no coverage in one EU country: Lithuania.

  1. c) Ratio: Engagement, follower growth and tweet volume in 2012

Both ratios “Engaging followers vs. follower growth in 2012” and “Engagement volume vs. tweet volume” are also two relevant set of figures that I consider worthy of examination, when analysing the Twitter engagement of the three account holders (Table 4).

Table 4: Engagement, follower growth and tweet volume

Table 4: Engagement, follower growth and tweet volume

The highest and lowest ratios, which indicate the percentage of “engaging followers” from the total of the “follower growth”, is respectively 6% (1332/21097) in September and 1% (166/19025) in July 2012.

The ratios indicating “engagement volume” vs. “tweet volume” are 43% (184/426) highest in March and 23% (72/318) lowest in May 2012.

Figure 5: Ratio - Engagement, tweet volume & followers

Figure 5: Ratio – Engagement, tweet volume & followers

The engagement algorithms introduced previously are clearly the early stages of establishing standard engagement formulae, which should contribute to obtaining more relevant information leading to better and relevant judgements.

Previous articles on the same subject

Tweet me a URL and make your communication richer

Why mentions on Twitter help people communicate

The European Commission’s EMPL presence on Twitter in 2012: Content languages and hashtagging

Why mentions on Twitter help people communicate: The European Commission’s EMPL presence on Twitter in 2012

[1] http://www.socialbakers.com/

[2] http://www.kaushik.net/avinash/best-social-media-metrics-conversation-amplification-applause-economic-value/

[3] http://klout.com/

[4] http:// http://www.socialresearchmethods.net/kb/scallik.php

Giving credit on Twitter

The European Commission’s EMPL presence on Twitter in 2012: Favourited tweets and replies

Favouriting a tweet is a Twitter function copied from a browser menu (options), when favouriting or bookmarking a webpage. When a user favourites a tweet, the author of the original is notified. In the case of our research question, it is important to find out who favourited the tweets, what are their user profiles and location. This piece of information provides evidence on potential disseminators and the favourited tweets, which are relevant engagement parameters.

Generally speaking a favourite on Twitter is seen as a way of giving credit. It is somehow similar to the Facebook (FB) ‘Like’, which has seen a much higher adoption, being more popular among the FB users. A “favourite” has a lower level of commitment than a RT and it is less public. One can also favourite a tweet for a later use since it is saved in the “Favourites” section of each user.

The three Twitter account holders benefited from this option being used by the followers as shown in Table 1.

Table 1: Favourited tweets, occurrences by user and favourited tweet reciprocity

Table 1: Favourited tweets, occurrences by user and favourited tweet reciprocity

The statistics indicate that EURes leads with 35% of favourited tweets with an occurrence of 70%, probably because of the practical information provided through guidelines to job-seekers. The three account holders favourited each other’s tweets as shown in Table 1.

It appears that favouriting each other’s tweets was not a priority of the three account holders as they preferred to retweet more than favouriting each other’s tweets.

A number of 138 Twitter users favourited 14% of the Social Europe tweets with an occurrence rate of 20% (Figure 1). 159 Twitter users favourited 35% of the EURes tweets with an occurrence rate of 70%. 121 Twitter users favourited 23% of Commissioner Andor’s tweets with an occurrence of 29%.

Figure 1: Favourite tweets

Figure 1: Favourite tweets

On average, 418 Twitter users favourited 21% of all the three user tweets, with an occurrence of 33%. The favourited tweets are discussed in a forthcoming article that focuses on “Engagement”.

Replies to tweets

A Twitter reply is a tweet sent in direct response to another tweet. The response is linked to that tweet. On the Twitter timeline, when someone clicks on the concerned tweet the page expands to show the reply, so both the tweet and the reply are paired. In recent times, replying has become common practice on Twitter. Replies also provide evidence to establish a degree of interaction, the so called “engagement rate”, when a reply author reacts to a tweet author.

Figure 2: Replies, recipients/mentions and their occurrences

Figure 2: Replies, recipients/mentions and their occurrences

There were 142 senders (Figure 2) who replied to the three account holders 197 times (139% occurrence). The tweet replies included 63 recipients or mentions which were recorded 262 times (416% occurrence). The top recipients/mentions with at least 5 occurrences are: @LaszloAndorEU (107), @EU_Social (38), @EURESjob (37), @BarrosoEU (9), @EU_Commission (9) and @MartinSchulz (5).

Commissioner Andor is on top of the list because, in addition to his mentions employed in the tweet bodies in 2012, he hosted a Twitter chat where the participants placed his username at the beginning of the tweets, which is a regular practice with Twitter chat. It is also worth mentioning the three account holders that came on top of the list, Social Europe and EURes, followed by EC President account, the European Commission corporate Twitter account and the European Parliament President’s account.

Retweeting as a means of taking part in a wider conversation

The European Commission’s EMPL presence on Twitter in 2012: Retweets

A retweet (RT) is a powerful Twitter function that enables republishing another user’s message on the Twitter platform. A RT is mainly associated to the multiplying effect that is generated when reposting another user’s tweets.

A statistics on RTs is an important engagement parameter to consider when establishing information multipliers on Twitter when answering the question of this research project. Statistics on how many RTs, their authors, profiles and locations are also discussed in relation to engagement in the next articles.

The retweeters (RTers), the users who showed an interest in some of the tweets published by Social Europe, EURes and Commissioner Andor, reposted a number of tweets on the platform.

RTs and their occurrences

  • 64% of the Social Europe’s tweets were retweeted at an occurrence rate of 157%.
  • 64% of the EURes’s tweets were retweeted at an occurrence rate of 453%.
  • 71% of the Commissioner Andor’s tweets were retweeted at an occurrence rate of 396%.
  • 66% of all tweets were retweeted at an occurrence rate of 302% (Figure 1)
Figure 1: RTs and occurrences

Figure 1: RTs and occurrences

The retweeting rate of 2/3 of the entire tweet volume of the three account holders indicates a high interest of the followers to redistribute content.

Categories of retweeters

The information on RTers was coded and grouped into six categories, according to their Twitter profiles:

  1. EU bodies and staff: European Commission, EU Delegations to non-EU countries, European Commission Departments (Directorates-General), European Commission Representations in the Member States, other EU bodies, Europe Direct Centres in the Member States, EU staff, EU Commissioners.
  2. Academia: universities, researchers, teachers, students.
  3. Experts: graduates, lawyers, project managers, Chief executive officers (CEOs), consultants, advisers, Non-governmental organisations (NGOs).
  4. International bodies: organisations, others than EU bodies.
  5. National bodies: national agencies, ministries, other national, regional and local structures.
  6. Public figures: journalists, bloggers, activists, writers, artists, politicians, Members of the European Parliament (MEPs) and opinion leaders.

In case a RTer had a multi-profile, that is more than one job listed in the profile, the first was considered. Usually people introduce themselves on Twitter with their main job and occasionally list their second job and/or hobbies.

Retweet occurrences by category of retweeters

The statistics of “RT occurrences by category of RTers” (Figure 2) indicate that more than a half of occurrences were generated by experts (55%), followed by national bodies (13%) and EU bodies and staff (13%). The other categories follow, with International bodies in last position where few occurrences were generated (1%)

Figure 2: RT Occurrences by category of retweeters

Figure 2: RT Occurrences by category of retweeters

The group of experts were the most interested in redistributing the content of the three account holders while EU bodies and staff contributed to redistributing this content either by helping with the content promotion or by being involved in the production of the content (policies, events, publications etc.). The remaining categories of retweeters expressed some interest in redistributing the content for the following reasons: tailoring content to suit their audiences (international and national bodies, public figures) or for research purposes, when involving academic key figures in national and European events (keynote speakers, contributing researchers to a number of studies etc.).

Number of RTs by country

With their Twitter communications in 2012, the three account holders benefited from being retweeted by other users from almost all EU countries and outside.

Top 3 RT Statistics all accounts

Table 1: Top 3 RT statistics all accounts

Table 1: Top 3 RT statistics all accounts

The tweets contained in Table 1 were the most popular in 2012. They related to certain events and policy outcomes. These are as follows:

1) Social Europe with the European Year 2012 for Active Ageing and Solidarity between Generations (EY2012), Youth Employment Chat, Poverty Convention, and the European Health Insurance Card, one of the most popular policy outcomes, an application for smartphones;

2) EURes with European Jobs Day (EJD) related information;

3) Commissioner Andor with policy related information and Youth Employment Package.

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