A new version of Tweetvis has been released and is available in the Downloads section. This is a maintenance release that fixes a few reported bugs associated with data file loading and pre-processing.
Author Archives: ch_admin
In the few years since the advent of ‘Big Data’ research, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of ‘grand’ social science methodological concepts for new social media research (i.e. they focus on general issues such as sampling, data validity, ethics, etc.). By contrast, we advance an abductively oriented methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool – Chorus – to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions.
Harvesting and visualising ‘big’ social media data is an increasingly feasible practice for social scientists. Yet whilst there is an emerging and substantial body of literature utilising social media as a data resource, there are a number of computational issues affecting data collection and analysis that for the large part remain hidden from the researcher’s view but which may problematise the findings we can legitimately draw from social media. This chapter outlines and explores two such issues as they occur for data taken from Twitter , commenting on how they might be handled in the undertaking of digital social science research. Here, we agree wholeheartedly subscribe to with Procter et al.’s insistence ‘that social researchers be trained in the underlying concepts of computational methods and tools so they can decide when and how to apply them’ (2013: 209). As such, we aim to outline highlight certain technical features and/or constraints pertaining to the collection and processing of Twitter data. In doing this we aim to, thereby helping help researchers to incorporate a technical understanding of the mechanics of digital research tools into robust and thoughtful analyses of their data.
A maintenance release that fixes some bugs identified in the recent version update. This can be downloaded from the Downloads page.
Dr Phil Brooker reflects on his recent experience of introducing early career researchers to the visual analytic approach
On October 30th, I got the chance to facilitate a 3-hour workshop on doing social media analytics with Chorus, and I’m happy to report that it was a great success!
The workshop formed part of a wider two-day training module, ‘Hearing, Seeing, Doing: Reflexivity, Creativity and Collaborations in Research’, for doctoral students across GW4 universities. The wider course was designed to engage early career researchers in thinking differently about the research process, and the Chorus team was drafted in to lead a workshop on a creative non-traditional/alternative methodology.
Reflecting on the use of Chorus as a pedagogical tool, I think the combination of visual models of data and the functions that help users navigate around that data to get back to original tweets was something that students could really get their teeth into. I started the workshop with a talk that set out a ‘visual analytic’ approach to social media data, wherein the visualisations you can see on-screen are not to be viewed as results, but as part of a data exploration process which can lead you back to interesting insights from the original data. From here, students were given chance to use Chorus themselves, first to explore a mock dataset and get a handle on how Chorus operates, then to undertake some data collection and visualisation work themselves on topics relevant to their own research. With the visual analytic approach in mind, the students seemed to get a lot out of probing the Twitter API for interesting data, then finding their way around it with the various visual models Chorus builds. I was careful to emphasise that there’s no such thing as ‘bad data’ – if they were having trouble drawing insights from the data they captured, I encouraged students to see this as an opportunity to learn what was lacking in their data collection strategy and see if they could use that information to go back and collect data better suited to addressing their questions. The interplay between data collection and data visualisation that Chorus offers made this a straightforward process, with students being easily able to switch between the two and reflect on how the data collection and visualisation process itself formulates part of their research as a ‘socio-technical assemblage’.
All in all, Chorus proved to be a useful tool for not only demonstrating how to capture and visualise Twitter data, but also for showing how to discover insight and master the research process as it moves between human and computational control. This seems to me to be a great asset to the software – it not only has a use as a tool for undertaking research work in social media analytics, it can help researchers see and understand the underlying methodological thinking that that research work embodies, allowing for a deeper exploration of the topic at hand.
If anyone is interested in using Chorus as part of their teaching, or in hearing more about the workshop course mentioned above, please do get in touch at firstname.lastname@example.org.
A new release of Tweetcatcher (TCD 1.3.0) is now available. This incorporates many bug-fixes, new functions and usability improvements. Thanks to all of you who provided feedback via email and the recent survey. We hope this new release improves the utility of TCD for research as well as the overall user experience. This is a beta release, but will be incorporated into the standard installation package soon. Please provide feedback (email@example.com) if you spot any remaining/new bugs.
TCD 1.3.0 can be downloaded here. After copying to your Chorus folder, make sure to rename the file to TCD.exe to ensure any short-cuts still work.
- Bug fixes
- Presence of inverted commas no longer causes Tweet text to be truncated
- Unicode character codes (e.g. \ud0d) are now intercepted and either converted into an ascii equivalent or an otherwise informative tag. For instance, non-latin characters are replaced by general tags (e.g. [ar] = Arabic, [el] = Greek)
- Retweet deletion is much faster now for large datasets
- Sorting by numerical fields (e.g. followers) now works properly
- Non-UK datetime formats (e.g. mm/dd/yyyy, yyyy/mm/dd) are now handled correctly. Previously this caused TCD to inexpicably retrieve zero tweets for some users. Datetime conversion should also work for all formats now.
- History lists allow easy navigation back to recent user timeline and query search sessions; last query search loaded automatically (data and query)
- More powerful and usable tools for building, exploring and maintaining user lists
- Right click existing user or enter screenname to get friends/followers
- Build user list from displayed Tweet set
- Add single users to list directly from Tweets or from user input
- Remove single users from a user list
- View user profile metadata for any user in list
- Full metadata table of any user list can be copied to clipboard using the menu for e.g. to allow editing of lists in Excel. Edited lists (screen names only) or full tables can be pasted back to TCD using the menu
- New features for analysing and manipulating Tweets
- Navigate directly to embedded URL from Tweet using context menu (right click)
- Navigate to user Twitter page from Tweet using context menu
- Delete selected tweet or all tweets by the author of a selected tweet (context menu)
- Filter Tweets table by keyword or phrase
- Setting to save column headers with a Tweets file (useful if exporting to Excel etc.)
- Removed several less useful fields from the Tweets table and replaced with more useful ones:
- Other UI improvements
- Presentation of session log is now more informative and compact
- Adjustments to column widths on Tweets table are now remembered next time TCD opens
- Button linking in Search frame to Twitter page providing query syntax help
- Reinstatement of the Cancel operation button
- Unsaved data status is now shown by means of an asterisk on the filename in the title bar
- Usability improvements wrt to the data save process (e.g. will prompt to save unsaved data if running a new query, warn before over-writing existing files etc.)
Dr Panos Panagiotopoulos is a Lecturer in Management at Queen Mary University of London
Public authorities mainly use social media to communicate with citizens. But they can also use networks like Twitter and LinkedIn to link people with expertise within the public sector. Unfortunately we still know little about how public officials use social media in this context. This article reports new research findings about these networks, from a study of tweets from the Twitter hashtag #localgov. We find that the pattern and direction of Twitter communication in government itself facilitates internal networking while reflecting the structure of power in the British state.
Read the conference paper on which this article is based here
By Panayiota Skouroumouni, MSc in Management and Organisational Innovation, Queen Mary University of London
For non-profit organisations, Twitter can be a powerful tool for network building and online engagement with key audiences. Since most non-profits operate with minimum resources, it is important that they have the necessary capacity to monitor engagement and make the most our of their social media presence.
As part of an MSc dissertation in Management and Organisational Innovation at Queen Mary University of London, Chorus tools were used to capture Twitter data from non-profits that focus on humanitarian campaigns related to children. With the help of Tweetcatcher, one years worth of data from July 2013 to July 2014 were captured from a selection of organisational accounts – the total number of tweets collected was 15,549.
Tweetvis was then used to understand the patterns of users’ activity through examining structural characteristics of the tweets (e.g. hashtags, retweets, mentions), as well as keyword frequency and sentiment around specific events. Identifying the most frequent words was important as an overview of the most important topics discussed. Hashtags revealed how children-related campaigns were linked to other news and worldwide events.
The study’s main findings how non-profit organisations are trying to build their network through online relationships in several ways, for example:
- Creating and heavily promoting tweets for causes that tend to receive a lot of attention (mentions, retweets).
- Mentioning celebrities in their tweets, who receive a lot of publicity and can lead others to engage with their causes.
- Mentioning/retweeting followers and supporters to involving them more actively in the organisations’ campaigns and possibly expand their influence through further networking effects. This can possibly enhance feeling of belongingness and connectedness among the audience, hence leading to more permanent connections and future action. A common reason of mentioning followers was acknowledgement of support.
- Creating and promoting hashtags – Each hashtag usually represents an event, which forms a community of users with the same interests, involved in the same conversation.
- Joining world-wide conversations about major events that attract the attention of Twitter users – During the time of this study, these included: the 3rd anniversary of conflict in Syria, the escalating war in Gaza, the abduction of schoolgirls in Nigeria, the typhoon in Philippines and the World Cup of June 2014 taking place in Brazil. A more advanced tactic used to attract attention to was “newsjacking”, where campaign-related content is inserted into contemporary media coverage like popular or breaking news. For instance, this infographic was created by the charity Save the Children during the London tube strike and received a lot of attention as well as controversy.
This new release contains some small but useful updates, mainly to do with the Cluster Explorer, including:
- A new function for node scaling creates a more balanced, legible view.
- Edges between nodes are now coloured grey to create a less cluttered/dense image without loss of information.
- The word map now offers finer control over the label filter and also the facility to manually increase/decrease the size of nodes.
- The menu bar has been reorganised such that Main Timeline functions come first, followed by the Term Timeline.
- The user now has has a choice of visual encodings by which to represent the term timeline (accessed from the menu: Term Time Line -> Cell Style.
- Improved semantic clustering of nodes in the interval and tweet cluster views