What Twitter suggests for the New York primary

With Donald Trump's overwhelming dominance, there are no surprises on the Republican front. However, the remaining primary elections hold the answer to perhaps the most important question for the Democrats: Clinton or Sanders? According to Twitter, Sanders. 

In order to gain a better understanding of the possible outcomes of the upcoming, and highly critical, New York primary, our team at Diktio Labs took a different approach and left the polls behind. Instead, we monitored and analyzed activity on Twitter. 

Between April 11 and April 15, 2016, we analyzed 151,965 tweets by 36,703 accounts containing the hashtag #NYPrimary, with the purpose of identifying key influencers, topic clusters, and of course the popularity of candidates in the Empire State. 

#Trump2016 vs. #FeelTheBern

Despite the fact that Hillary Clinton is more widely considered to emerge victorious from the NY primary, and eventually become the candidate of the Democrats, Bernie Sanders and his supporters have a much stronger online presence. In fact, Sanders is the only candidate, thanks to his active supporters, who has a slight chance of diluting Trump's online dominance. 

Sanders supporters tweet vigorously, hence they represent over 50% of the Top 30 most active accounts. (We have manually removed bots.)

When it comes to influence however, results look slightly different. The Top 30 most retweeted accounts are in favor of Trump, but only with a mere 47%.

We categorized the most influential accounts by different dimensions of: well-connected, most mentioned, accounts with most outward activity and community brokers.

Well-connected accounts are those that receive the most attention in the NY primary conversations on Twitter from other accounts that have many followers. Trump’s supporters dominate this list. Noticeable are media outlets, such as NY1, CNN, POLITICO, and ABC News, and journalists, like Jeanine Ramirez covering stories around the primaries, who are often cited and followed by users interested in the primaries.

For most mentioned, there is only one account that makes it to the list for Cruz, four supporting Clinton, while the rest of the top 30 are Trump and Sanders supporters.

Most outward activity: accounts producing the most tweets in support for a candidate seem to be overwhelmingly pro-Sanders. However, most of them do not make it into the well-connected list because they are rather poorly embedded into Twitter conversations around the election.

    

Community brokers are accounts that bridge conversations about their preferred candidates between groups of users who would otherwise not communicate. They mostly engage in debated topics by following conversations in other camps and replying to those.  

In our analysis, we also examined the top 30 hashtags mentioned in order to gain a better understanding of the topics surrounding the New York primary.

Many hashtags are used to reference events or places. They are usually used in conjunction with candidate-related hashtags to mobilize followers to join rallies, follow ongoing or future events, and encourage voting.

Trump’s campaign-related hashtags receive the most attention, followed by Sanders. Clinton is referenced mostly through the hashtag #ImWithHer, rather than nominally. The supporters of Cruz mention their preferred candidate much less than the other supporters.

The hashtag #NeverTrump is considered the only negative tag in the Top 30.

In order to identify the top 30 most mentioned (interesting/debated/adored/hated) candidates, we took a look at candidate mentions, on a user level. Example: how many users talked about candidates, during the analyzed time frame. We have visualized our results on a network map. The size of nodes and labels indicates the number of users mentioning the name of the candidate, while the connecting lines (edges) reflect how many users talked about both candidates. The color of the node indicates party affiliations: red is for Republicans, blue is for Democrats.

The most mentioned candidate was Donald Trump with more than 2,000 more users talking about him than about Bernie Sanders. Kasich was the least mentioned candidate. Clinton and Cruz are mentioned almost by the same number of users, which is less than half the number of users mentioning Trump.

In terms of users talking about two candidates, the biggest overlap is between Clinton and Sanders, followed by Trump and Cruz. The difference is more than 1,000 users. The remaining combinations were not so frequent apart from that of Clinton and Trump, mentioned by two percent of the users using the hashtag #NYPrimary.

This interactive, clickable and zoomable network map, (filtered to indicate top connections only) depicts what hashtags were mentioned together within the same tweet in our dataset. The thicker the edge, the more often the two hashtags were mentioned together. Node and label sizes indicate how often the given hashtag was mentioned together with other hashtags [network science metric: weighted degree].

Over 19% of the tweets associated #NYPrimary with #Trump2016, and almost 16% with #FeelTheBern. #ImWithHer: 8.6% of tweets.  The different color clusters depict various topics around the NY Primary. Dark orange hashtags represent upcoming primaries: #PAPrimary, #MDPrimary, #DEPrimary, #CTPrimary, #RIPrimary, dark blue ones are the most associated hashtags with New York, while the light orange hashtags with the Democratic party. 

Will the domination on Twitter manifest in the elections?

So far, our analyses clearly show that Trump and Sanders dominate Twitter conversations about the NY primary, each in their own community of supporters and opponents. The NY primary are expected to have a higher than average turnout in the state of New York, which might bring surprises, at least for the Democrats, where, according to polls, the two candidates are in a tight race for the win.

Please bear in mind that an online analysis does not intend to replace the offline polls or in itself cannot predict the results of the actual elections. Our experience in political analysis suggests that smaller parties and “anti-establishment” candidates tend to be more active on social media. The NY primaries are further complicated by the fact that the registration was over six months ago, thus however successful a candidate is on social media now, it might be too late to turn the popularity gained into actual votes. Given the race is not over between the candidates, there is a lot to learn in the forthcoming months.

Are you interested in the methods behind our analysis, and how you could benefit from online community mapping? Reach out to us via email at info@diktiolabs.com

How the HR Tech Europe conference went down on Twitter

HR Tech Europe, the self-proclaimed fastest growing HR event in Europe, took place on March 24-25 in London. The conference drew HR practitioners from far and wide to discover the latest trends in HR technology. Although we could not be there in body to showcase our cloud-based organizational diagnostic tool, OrgMapper, we wanted to show some spirit and decided to map the social activity around the event.



How we did this:

In order to map the structure of the online community around the conference, we tracked Twitter with our online community mapping suite, Diktio Labs, between 6 am, March 18, 2015 and 11:50 pm March 26, 2015, and looked into the network of all relevant interactions. We downloaded 11,013 tweets containing the hashtag #HRTechEurope.

Each node (blue dot) in the below network represents a Twitter account; the more influential a user is, the darker and larger its node will be. For a better visual experience, the network is filtered to only depict edges (connections) that form amongst people who retweeted each other at least three times. The thickness of the edges indicates the number of retweets; meaning, the thicker the edge, the more often a user retweeted another user. Arrows indicate the direction of the retweet: the head of the arrow points at the retweeted user.

Click here to enlarge

Our key findings:

@hrzone was the most retweeted user with 493 retweets, followed by @oraclehcm, @let_anita, @david_green_uk, @mervyndinnen, and @rohitbhosale. The network is well-connected, though @rohitbhosale and @emee_insights form almost a separate cluster but still connected via @violazoldy.

We also took a peek at the most trending hashtags, but excluded #HRTechEurope (which was used extensively throughout this period). We found that #HR was the most widely used hashtag, with 605 mentions within the community, followed by the hashtags #ngarh and #Gamification.

In 2015 online influencers can make or break a brand. Instead of concentrating on HOW MANY people are talking about your business, focus on WHO is in the middle of these discussions. Great marketers know and engage their social media influencers. Building close relationships with your influencers will not only help strengthen their advocacy, but also convince them to get involved in your communication campaigns.

For more information on online influencer identification, contact us at info@diktiolabs.com.

The Harlem Shake Story - aka. Birth of a Meme

If you still have not heard of the Harlem Shake you must be living in a cave. Much has been written about the rapid and global spread of this catchy internet meme, yet little is understood about how it spread. A series of remixed videos along with a number of key communities around the world triggered a rapid escalation, giving the meme widespread global visibility. Who were the initial communities behind this mega-trend? SocialFlow took a look at 1.9 million tweets during a two-week period that included the words ’harlem shake’, or some versions of it.

The Harlem Shake itself is a dance style born in New York City more than 30 years ago. During halftime at street ball games held in Rucker Park, a skinny man known in the neighborhood as Al. B. would entertain the crowd with his own brand of moves, a dance that around Harlem became known as 'The Al. B. Though it started in 1981, the Harlem Shake became mainstream in 2001 when G. Dep featured the dance in his music video "Let's GetIt". While mining Twitter data, references to Harlem Shake (the original dance) were seen quite often prior to it becoming a popular meme. When someone tweets, "I just passed my final exams! *harlem shakes*," it's the equivalent of saying "I just passed my final exams! Look at me dancing!" While Bauuer's now infamous track was released on Diplo's Mad Decent label back in August 2012 (posted to YouTube on August 23 2012), it only accrued minor visibility for the first few months. Then February hit, and something changed.

The timeline below highlights the very first days as the meme was taking off. In blue, we see references to the 1980's dance *harlem shakes*, while the green curve represents Tweets that use the phrase 'The Harlem Shake', many of them linking to one of the first three versions of the meme on YouTube.

On February 2, The Sunny Coast Skate (TSCS) group establish the form of the meme in a YouTube video they upload. On the 5, PHL_On_NAN posts a remix (v2), gaining 300,000 views within 24 hours, and prompting further parodies shortly after. On Feb. 7, YouTuber hiimrawn uploaded a version titled "Harlem Shake v3 (office edition)" featuring the staff of online video production company Maker Studios. The video becomes is a hit, amassing more than 7.4 million views over the following week, and inspiring a number of contributions from well-known Internet companies, including BuzzFeed, CollegeHumor, Vimeo and Facebook.

Social Flow looked at the social connections amongst users who were posting to the meme. This gave them the ability to identify the underlying communities engaging with the meme at a very early stage. In the graph above each node represents a user that was actively posting and referencing the Harlem Shake meme on Feb 7 or 8 to Twitter. Connections between users reflect follow/friendship relationships. The graph is organized using a force directed algorithm, and colored based on modularity, highlighting dominant clusters - regions in the graph which are much more interconnected. These clusters represent groups of users who tend to have some attribute in common. The purple region in the graph (left side) represents African American Twitter users who are referencing Harlem Shake in its original context. There's very little density there as it is not really a tight-knit community, but rather a segment of users who are culturally aligned, and are clearly much more interconnected amongst themselves than with other groups.

After a similar analysis on the following two days (Feb 9 and 10) different communities can be seen emerging, resulting in a much more tightly knit graph structure. While the same dense cluster of musicians and DJs (in turquoise) still exists, there are substantially more self-identified YouTubers both across the US and the UK. At the same time there's a significant gamer / machinima cluster that's also participating, as well as a growing Jamaican contingent, and quite a few dutch profiles (purple -- left). Additionally, we see various celebrity and media accounts who caught on to the meme -- @jimmyfallon, @mashable and @huffingtonpost. By capturing the two snapshots, we can also make sense of the evolution of the meme as it becomes more and more visible. At first, loosely connected communities separately humored by the videos. Within days, we see major media outlets jump on board, and a much more intertwined landscape. We see different regions in the world light up, and identify communities of important YouTube enthusiasts who effectively get this content to spread.

Memes have become a sort of distributed mass spectacle, a mechanism that both capture people's attention, and define what is "cool" or "trendy." We see more and more companies and brands try to associate themselves with certain memes, as a way to maintain a connection with their audience, gain the cool factor. Pepsi did this with the Harlem Shake and saw an incredibly positive response. 

As we get better at identifying these trends and trend-setting communities early on, the pressure to participate will rise. As social networks become globally-intertwined, we're witnessing a growing number of memes conquer the world at large. These moments are critical points in time, where there are significant levels of attention given towards a specific entity - be it a joke, funny video or a political topic. Piecing together data from social networks can help us identify critical points in time, as well as the underlying communities and trendsetters for the humor-based memes, or the agenda setters for politically-slanted ones. The only question is: what will be the next one, cashing in on it 15 minutes?

Hungry for more? Read the full article on HuffPost.

Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter

The increasing impact of social media gives modern marketing a lot to think about; Facebook, Twitter, Tumblr, Flickr, Pinterest, Google+ and hundreds of blogs are only the tip of the iceberg, and it seems impossible to be up-to-date on all the channels. To look at them one by one seems illogical, since the key aspect of the generated content lays in the network effect, that enables the vast exchange of information. What remains to be done? This three-part series introduces Maven7’s newest research focusing on the network effect, and therefore making life easier for online marketing, PR, and product management experts.

In contrast to the Facebook-boom that began 2-3 years ago, and reached it’s 3 million user population in Hungary last year, the Twitter community seems to be growing at a slower pace. The Twitter company was launched in 2006 in San Franscisco, and has around 30 thousand Hungarian visitors a day, similar to the blog hosting site Tumblr.

Why bother with them at all – you may ask? The majority of Twitter and Tumblr users come from an urban environment, most of them are high-status people living in Budapest. Microblogs spread information – especially negative ones – very fast. Here is a comparison: a „tradiotional” online medium might be busy with a story for a whole week, whereas on Twitter – given that the right person spreads it – the same information is distributed within 2.5 hours! Therefore it is of great importance, to keep these outlets under control as much as possible. It is not a coincidence, that Hollywood celebrities like Charlie Sheen (with his 7.5  million followers) get paid around 50thousand dollars per tweet. Our survey conducted during Spanish election season showed that even an average person can have substantial effect on voters. This leaves no second thoughts about monitoring the information that gets to these loyal, high presitge consumers.

National key opinion leaders (famous journalists, bloggers, athletes) are active on multiple scial media platforms, but the small number of follower bases point to the fact, that the person with the most followers is not neccesary the most influental one, when it comes to information distribution. We need to find out, which tweeter is the most relevant one, and has the power to form opinions when it comes to our products. We can achive this through Twitter data using the methods of data mining. The user’s position in the network is another key factor (i.e. how many followers does the user have in common with our competing brand). Compared to Twitter, Facebook has open activity data, which means that we can easily access information regarding the users network of contacts.

Social Media and the Power of Networks 2. – Key Opinion Leaders

There are multiple ways we can build networks from the connections of Twitter users. First of all we can regard the distributors (people related to the brand,  or the brand’s official page) as the source of information, and link individual users to them, based on who retweeted the source’s message. Furthermore, the users themselves have followers and friends online, the latter one representing a stronger status, that can be interpreted as a network itself (for more, check our previous article on a follower- andfriend-based network). The picture shows a network of retweeted messages related to an FMCG product distributor and its competitors.

Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter pic 2.

The second picture represents the choice between data sources, that have the most influence on our consumer basis. The yellow boxes are the key opinion leaders(KOLs), who can reach out to the major part of the community in only three steps. They hold a central position in the network, because they have the biggest follower- and friendbasis.

Through analysis of Twitter data we can not only locate the key opinion leaders and characters of a brand, but with the help of location information we can also interpret product placement related research. A good example of using location data is our previous article on the optimallocalization af ATMs. 

To be continued.

Social Media and the power of networks 1. – The Hungarian blog scene

Because of the spreading of Social Media usage the modern marketing agencies age are just gazing. Facebook, Twitter, Tumblr, Flickr, Pinterest, Google+, hundreds of blogs and so on without end. It is impossible to follow so many sites up-to-date. Also, it is not enough to only eximes these sites seperately, because the essence of the content generated by the users is the network effect, which causes the fast information spread. What can we do then? In our series, where we collected some of the latest network research results by Maven 7, we show how these findings can facilitate the on-line marketing specialists’, the PR specialists’ and product managerers’ lives.

We may trace back the beginning of Social Media to the blogs.  These contents are not the ordinary Social Media model, as we understand it today, but here the contents are already generated by users, as a major attribute of web2.0. The largest benefit of blogging is that anybody can write anything in a topic, and so the number of topics is practically endless. In 2006, the early times of Hungarian blogging, it was yet easy to keep count of bloggers. Then, thanks to its easy use, it rapidly started to increase.

But why are blogs so important for us after all? Considering that already more than ten thousand blogs exist and their reading blooms, a company needs to know who the Key Opinion Leaders are within the blog space, who have impact on their consumers. Network research provides help exactly in this. With the help of our unique methodology we can show who are the Key OpinionLeader is of a given topic, and we also can forecast who are the Rising Stars of the next generation.

Our research identified the Key Opinion Leaders (KOLs) of IT related blogs within a Hungarian blog service provider.  According to the network approach we presume that readers of same blogs are interested in the same topics. So by examining the group of users and visitors, the most important characters of the topic can be identified. The importance of a blog not only depends on the number of visitors and clicks, but also that what kind of other blogs the same user reads at the same time. It is more expensive to advertise on a Key Opinion Leader blog (green node), than on an other blog, which is connected to it (red node). With our method anybody can identify new, relevant and not „used up” opinion leaders, and may also validate previous campaigns.

It is also an important question, which other topics are related to our professional field. For example, how much overlap is there between the community of IT blogs and the community of game blogs? The following picture shows how the IT community is connected to other blogs. The bigger the nodes are the more connection a given topic has to other topics.

                                                                                                              

Beyond the identification of blogs’ KOLs and Rising Stars we can identify KOLs of a specific topic within the whole Hungarian online space by using other sources from the Social Media, such as Facebook, Twitter, LinkedIn...

To be continued....

ATM localization

Now that the time of low-cost Canaan comes to Budapest soon,even more bunches of young tourists are expected. But where are they rmoving around the city? Are they really interested in  Buda Castle and Heroes Square? Do they really know the best party places of Budapest? Where do they usually have dinner? Where do they stay? We would like to introduce a case study of Maven7, which can give answers for these questions too.
With proper background information and approriate time parameters we can get insight into the latitude of tourists, especially during festival time. These information could have a significant role in tourism or festival-linked service optimization.
E.g. it can provide help in:
• optimization of location of linked services
• optimization of advertisement placement
• optimization of planning transfer
Our study case was based on the performance of two commercial banks’ ATMs located in district V,VI and VII in Budapest. The investigation was supported by territorially relevant Flickr-data.
The positions of ATM's were to some extent optimal, but on a number of occasions we have found a room for improvement. For instance as our findings show repositioning or installation of a new ATM at Vörösmarty square would significantly improve money withdrawal. The existing ATM is at Bajcsy-Zsilinszky subway station - a busy junction -, however, according to picture's geocodes tourists walk there quite seldom.

Our innivative approach is to utilise social media in optimising ATM locations. The thermographic image shows us straight where potencial clients move - numerically. Furthermore, on the grounds of Eric Fischer's study we can declare that different social media types attract different users' groups. For example Flickr data reflects the habit of tourists, while Twitter gives us an insight into residents' movements. When analysing Flickr data, we are able to differenciate between nationalities.