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fail Welcome to Etherpad for the #fail2016 workshop at #icwsm!

Please feel free to join in the discussion on "Things that didn't work out in social media research - and what we can learn from them"


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#fail experiences from the audience
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Please feel free to share any practical examples from your own work here. 



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Categories: what can go wrong in social media research?
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Please add / modify / comment!


Notes from 3rd workshop (ICWSM-16)

Keynote Munmun de Choudhury: 
    - studying mental wellbeing - which is often happening "offline". So how to combine different types of data to learn about online and offline behaviour. 
    - there are many social media where  you don't have to identify. This is important for people with mental health illnesses, as they often do not want identify. 
    - how to figure out that an observation (e.g. language change with new mothers)  something due to the specific cohort, or anything that you could generalize. 
    - LESSON LEARNED: you need ground truth data. You cannot study this by only looking at online data.
    - You have to involve the users: if you go into talking to the people you are studying, how do you scale? 
    - demographics: how do you measure the prevalence of depression - if you work with Twitter data you have to keep in mind that Twitter is not used equally across the US. 
    - interventions in social media environment: e.g. "thighgap" is blocked on Instagram as a searchterm. 
    - ethical challenges: what happens if people realize that they are being monitored, even if a researcher may have the intention to help them. 
    - does the duty to act apply, if the judgement of someone being at risk of suicide is done by an algorithm (e.g. based on social media data). 
    
Yenn Lee:
- Hyperconnectivity and Hybridity and Fluidity. 
- Anonymity / Pseudonyminty. 
- bamboo groves - accounts which display the password on the website, so that everyone can use them. 
- high time of this phenomenon was around 2012. Is research on it still relevant if you start studying it 1, 2, 3 years later. 
- psychological toll on researchers. 
- time: what effect does the time you do a study have on your resutls. 
- platforms.: cross-platform studies. 
- participation: how much should I involve myself into phenomena (e.g. when studying misogynic online platforms). 
--> boundary drawing. 
- dicision not to persue any interactions with indivuduals (because these people were really 
Comment from audience: "You always create social responsibility. You don't just start a new project, you get involved with people. It will also affect you personally."

Isabella Peters: 
- good thing about classic bibliometrics with Scopus etc.: you know the gold standard, you know how much information is in there. 
This is different in altmetrics. 
- popular do it yourself tools in altmetrics, because the approach is demographic. 
- we already know that results in altmetrics depend heavily on the tools and aggregators you may use. 
- disciplinary differences in how researchers use social media such as twitter. 
- compare different tools that are used in data collection. 
- bugs in data collection systems
- different entry barriers for tools: some tools work only with windows, some have to be paid
- no tool is looking at "all" social media platforms, so you always get a certain bias. 
LESSON LEARNED: different altmetric tools all perfom differently
LESSON LEARNED: setting up a data collection logic / search query is already difficult (e.g. collect DOIs to search them in altmetrics tool). 
LESSON LEARNED: we are likely to underestimate real numbers
- question: how do you increase comparability, collect all data at the same time. 
- challenge: pick the right time span. And be aware that social media usage may change across seasons (e.g. Christmas vs. exam time). 
- visualization is another challenge. 
- scalability. Some tools are not equipped for big datasets. 
- different concepts e.g. "like", "share" work different across platforms. 
Approach: NISO Altmetrics Data Quality Code of Conduct - desciption of how they treat their data. 








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Potential venues for future workshops?
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Things to do differently at next workshops?
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Tipps for logistics?


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Key aspects for workshop summary
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Which part of todays discussions should we report at future workshops?


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Notes from previous workshops:
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From first workshop (#websci15): 

Random notes:


Notes from second workshop (#ir16)