We are now in our 3nd year of running this event!
This year Dr Marc Smith (@marc_smith) from the Social Media Research Foundation joins us remotely including a Q&A session!
+ we are likely to include more speakers!
This year we also invite paper submissions of 500 words to be considered for presentation ! Email at Wasim.Ahmed@Northumbria.ac.uk
Papers Track Chair:
Dr Joseph Downing (London School of Economics and Political Sciences)
In intensive three days training participants will learn:
- To use available social media monitoring tools (by hands on experience)
- How to define goals on what metrics to monitor
- How to develop measurement metrics
- How social media monitoring and listening is used in different occasions
- To increase social media impressions and use social media to target different customer segments
- To use personality insights and machine learning to better understand consumers
- Insights from academic research involving use cases and new types of research that is possible
- What programming languages can offer that analytical tools do not
- We believe that it is best to learn using a range of theoretical and practical tools, as well as looking at case studies, and then applying the knowledge gained on getting hands on experience.
Our course is divided roughly into the following:
– Theory (20%)
– Collaborative peer sessions (20%)
– Present different tools and the functions of these (10%)
– Provide a practical work and hands on experience (50%)
Learning will begin before the participants engage in the course. As participants will be asked what they would like to get out of the workshop and so the content we deliver will be tailored to the needs of the delegate, as much as possible. We will also source and deliver a number of papers for the delegates (course reading).
This post is based on my 2018 journal article for Online Information Review titled ‘Social media analytics: analysis and visualisation of news diffusion using NodeXL’ .
In this paper I wrote that
“One well-known case of news emerging through Twitter before traditional media outlet was the death of Osama Bin Laden which was leaked on the platform (Hu et al., 2012). Moreover, Hu et al (2012) noted that one of the reasons for Twitter users to become convinced of this news was because the users who were posting the news appeared to be journalists and politicians i.e., reputable individuals. Twitter also has potential for citizen journalism because most smartphones are now able to capture an image on their device and have it uploaded to Twitter in under 45 seconds (Murthy, 2011). An iconic example of this is a passenger on the Midtown Ferry whom photographed a downed U.S Airways jet floating in the Hudson river in 2009 prior to the mainstream media even arriving to the scene (Murthy, 2011). These cases highlight the power of Twitter in the rapid cascading and diffusing information during emerging news events. ” (Ahmed, 2018 p3)
In the passage above I noted the role of Twitter as a tool for breaking news stories and citizen journalism. It is also important to note that Twitter is also used by politicians and journalists with particular affiliations to try and shape public discourse.
In the paper I argued that better tools and methods are required to be able to critically analyse, visualize and understand the swarm of content being generated by social media platforms such as Twitter. I noted that:
“However, it can be argued that Twitter has been poorly mapped and understood for its network properties by news media. This is because although it is possible to visualise the structure of a conversation on Twitter and to identify prominent users and the overall structure of the conversation in order to garner the situational awareness of an emerging news story this aspect of Twitter is seldom reported on by news media.” (Ahmed, 2018 p.9)
The tool that I outlined which can be used by journalists to map and visualise social media is known as NodeXL. In the paper I noted that:
“NodeXL allows end-users to generate network visualisations from a range of data sources and one such source is Twitter. In the case of Twitter NodeXL can additionally generate a number of metrics associated with the graphs such as:
- The most frequently shared URLs
- Word Pairs
- Mentioned Users
- Most frequent tweeters “
(Ahmed, 2018 p.5)
I then went on to provide a table for how NodeXL could be used by journalists.
|General Goals for Newsrooms||How to Achieve Goal|
Determine dominant external media narratives shared on social media during an evolving news event.
|Examine most frequently shared URLs, domains, and hashtags in NodeXL.|
|Establish different discussions that are taking place based on an emergent new development.||
Examine the different groups that emerge by examining the different groups and to interpret the most frequently occurring words, word pairs in order to understand the discussions that are taking place.
|Discover main information diffusers during a developing news story and/or a topic of interest.||
To identify users influential using the metric of betweenness centrality and/or to identify broadcast hubs within network visualisations which show prominent users.
|Learn about general key players during an emerging news event.||
To examine metrics within Twitter such as replied-to, mentioned users, and most frequent tweeters
Ascertain users who are concerned with an evolving news event.
|To locate users who have been tweeting the most.|
Table recreated from Ahmed (2018 p.8)
In the paper I provide the example of #MacronLeaks, however, below I have provided a network visualization of the keyword ‘Theresa May’.
Figure 1 – Network visualization of Theresa May
The above network graph resembles a number of different shapes and we can interpret them drawing on guidance from Smith et al (2014) which outlines different types of network structures.
Figure 2 – Different Types of Network Structure
We can then work through the full analytics (found here) and begin to complete Table 1 taking on board the guidance provided. For instance, to understand some of the main narratives we can take a look at most shared URLs, domains, and hashtags in NodeXL. This will then provide insight into some of the key narratives related to Theresa May on Twitter.
Hu, M., Liu, S., Wei, F., Wu, Y., Stasko, J., Ma, K.-L., 2012. Breaking News on Twitter, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’12. ACM, New York, NY, USA, pp. 2751–2754. https://doi.org/10.1145/2207676.2208672
Murthy, D., 2011. Twitter: Microphone for the masses? Media Cult. Soc. 33, 779–789.
Smith, M.A., Rainie, L., Shneiderman, B. and Himelboim, I. (2014), “Mapping Twitter topic networks:
from polarized crowds to community clusters”, Pew Research Center, Vol. 20, pp. 1-56.
Social media as a research tool has gained in popularity in recent years. Those new to the field may wish to know about the key methodologies and tools that can be used for the analysis of data. This post will provide a round-up of popular methods and tools for the analysis of social media data.
Although there will be a number of ways to build custom scripts for the analysis of data; it remains that pre-existing tools remain popular for social science scholars as it removes the need for development skills. Twitter remains the most utilised platform and will be the focus of this post.
As a wrote in my 2015 LSE Impact blog post:
- Sentiment analysis works well with social media data, as posts may be consistent in length
- Time series analysis is normally used when examining posts overtime to see when a peak of social media posts may occur
- Network analysis is used to visualize the connections between people and to better understand the structure of the conversation.
- Machine learning methods may work well with social media data because of the volume of tweets
- Qualitative analysis methods (such as thematic and content analysis) are rare for social media research, however, they can often offer up more depth than quantitative methods.
Read more here
In terms of popular tools for the analysis of social media data a list of popular tools is populated below:
An overview of tools for 2019
|Tool||OS||Download and/or access from||Platforms*|
|Chorus (free)||Windows (Desktop advisable)||http://chorusanalytics.co.uk/chorus/request_download.php|
|COSMOS Project (free)||Windows
MAC OS X
|Mozdeh||Windows (Desktop advisable)||http://mozdeh.wlv.ac.uk/installation.html|
|NVivo||Windows and MAC||http://www.qsrinternational.com/product||Twitter
Ability to import
|Twitter Arching Google Spreadsheet (TAGS)||Web-based||https://tags.hawksey.info|
|Webometric Analyst||Windows||http://lexiurl.wlv.ac.uk||Twitter (with image extraction capabilities)
Other web resources
The tools above can be used in a manner to conduct academic research that many may believe that is not possible! Consequently I am running a 1 day intensive training event teaching the above skills and techniques on May 17th in London (online attendance will be made possible). Information provided below:
Social Media and Digital Humanities: Methods and Approaches For Social Scientists and Digital Marketing Professionals
This event will introduce a mix of methodologies and provide an overview of free-to-use and commercial software for the analysis of social media data. It will be led by an expert in the area, Dr Wasim Ahmed who has taught Undergraduate, Masters, and PhD level courses on social media data analytics.
Social Media and Digital Humanities: Methods and Approaches For Social Scientists and Digital Marketing Professionals
1-day intensive training event in central London
This event will introduce a mix of methodologies and provide an overview of free-to-use and commercial software for the analysis of social media data.
It will be led by an expert in the area, Dr Wasim Ahmed who has taught Undergraduate, Masters, and PhD level courses on social media data analytics.
Register here: https://event.gg/4991/
On the 11th of January 2019 I graduated from the University of Sheffield with a PhD which was titled “Using Twitter data to provide qualitative insights into pandemics and epidemics“.
New publication: A comparison of information sharing behaviours across 379 health conditions on Twitter
A comparison of information sharing behaviours across 379 health conditions on Twitter in International Journal of Public Health
To compare information sharing of over 379 health conditions on Twitter to uncover trends and patterns of online user activities.
We collected 1.5 million tweets generated by over 450,000 Twitter users for 379 health conditions, each of which was quantified using a multivariate model describing engagement, user and content aspects of the data and compared using correlation and network analysis to discover patterns of user activities in these online communities.
We found a significant imbalance in terms of the size of communities interested in different health conditions, regardless of the seriousness of these conditions. Improving the informativeness of tweets by using, for example, URLs, multimedia and mentions can be important factors in promoting health conditions on Twitter. Using hashtags on the contrary is less effective. Social network analysis revealed similar structures of the discussion found across different health conditions.
Our study found variance in activity between different health communities on Twitter, and our results are likely to be of interest to public health authorities and officials interested in the potential of Twitter to raise awareness of public health.
The full paper can be accessed here: https://link.springer.com/article/10.1007/s00038-018-1192-5
I recently provided an expert comment statement on the Emoji Cities tool and I thought that it may be of interest to readers. Check it out below!
Every city is different, but how different? From Rio to Shanghai and London to LA, Emoji Cities uses Twitter activity to examine what the world is talking about. Find out the top trends, the most popular hashtags, who’s in the news, and – of course – the top emojis for every major city. Discover what the world is saying – and feeling.
Check out Emoji Cities here: https://www.smartdestinations.com/emoji-cities/
Best Influencer Detection on Twitter: Using NodeXL Pro to identify influential Manchester United Fans
Recently I created a short video-summary of a project which collected longitudinal data on Manchester United FC over the course of the 2017/18 season using NodeXL Pro. Be sure to watch the video below.
These days I am working as an Assistant Professor at Northumbria University, recently, I presented a paper based on my PhD research, recently completed at the Information School based at the University of Sheffield, at the 9th International Social Media and Society Conference which had an acceptance rate of 47%.
The paper titled Moral Panic through the Lens of Twitter: An Analysis of Infectious Disease Outbreaks can be accessed here. The paper was also summarised in a blog post by Professor Axel Bruns, President of the Association of Internet Researchers (AoIR).
At the awards ceremony, I received the award of ‘most engaged Twitter user’ and won a series of prizes (pictured above). The hashtag for the conference contained over 400 unique users, and generated over 2,500 unique tweets and became a trending topic in Copenhagen (where the conference was taking place).
Croatia’s World Cup consolation: Google searches soar as world seeks information on finalists
Croatia may have lost to France in the World Cup final, but the small Eastern European nation may just have won something altogether more precious – worldwide recognition on a whole new level.
Social media users are tweeting about Croatia like never before and web searches are through the roof. The country has long been a holiday hotspot, but many people don’t seem to know much more than that about it. That all seemed to change dramatically over the World Cup.
Croatia’s progress to the 2018 final was something of a surprise. Coming from a nation of just 4m people, the team displayed true character and grit to battle to the end to take on France, one of the favourites to win. And while Croatia didn’t take home the trophy, the nation is likely to benefit massively in other ways.
Google Trends data highlights, astonishingly, that Google web search queries for “Croatia” have increased to the highest levels in history as people around the world search and locate information using the keyword “Croatia”. Google ranks search popularity from zero to 100, where a value of 100 is the peak popularity for a term, and 50 half as popular. In the 2014 tournament, Croatia’s score was 28, in 2018 the score is 100.
In a period of just one hour during the semi-final, 350,000 tweets were also sent out including the word “Croatia”. That’s roughly 80 times more than on an average day. And towards the end of the night on the day of the final against France, more than a million tweets had been sent out that included the word “Croatia”.
A country would usually have to spend millions if they wanted to gain this type of interest. This itself is a very rewarding aspect of progression in the tournament as it equates to massive free exposure for a nation.
Your next holiday?
Even before the final, the Croatian tourist board announced it had observed a 250% increase in website visits from across the world, compared to the same time last year. Croatian tourism outlets also capitalised on the increased interest by launching specific marketing communications across social media. This included a promotional video shared on YouTube by
the Croatian National Tourist Board which has been viewed over 250,000 times.
Meanwhile, the Croatian economy seemed to be enjoying a boost from World Cup interest at home. The national tax administration indicated an increase in spend during the tournament. Individual stores reported a 400% increase in sales as compared to previous years as locals stocked up on beverages, snacks, and television sets.
Although it was not the dream end for a Croatian team which showed extreme courage in reaching the final, progressing in the tournament has led to a monumental increase in the digital footprint of the country. That, in turn, has the potential to deliver tangible benefits to the Croatian economy and the tourism sector.
More evidence-based articles about football and the World Cup: