Idle No More at two months: public sentiment (part 4/6)

Idle No More at two months: public sentiment (part 4/6)

Sentiment-in_postAnalysis of sentiment was conducted for three blocks of time: December 1-15, December 16-31 and January 1-19. Sentiment was broken down into four categories: positive, neutral/no sentiment, negative and noise.

For the purpose of this analysis, I looked only at tweets issued by people who contributed 15 or fewer tweets to the Idle No More chatter. The rationale is this form of analysis is more likely to give me a ‘truer’ sense of public sentiment and exclude the presupposed support associated with members of the Idle No More movement.

More context… this analysis considers “unknown gender” which I explain in my gender analysis. I generally wouldn’t consider unknown gender in isolation in my analysis. However, most of the noise (described below) originates in this group and I felt it was appropriate to try to isolate the noise as much as possible.


Let me explain noise. Noise is considered to be any tweet unrelated to the movement. In most cases, noise is issued by so-called SPAMbots (pieces of software which issue tweets). Whether issued by a SPAMbot or an individual, noise is a recognition that a particular theme or hasthtag has become remarkably popular and represents an opportunity to try to hijack attention for their own interests. This is typically a way to get clicks for porn or business/marketing/retail sites.

Sentiment: December 1-15

When the movement began, most of the participants were organizers and those close to the ideology that spawned the movement. Not surprisingly, this means a majority of the chatter was positive. In fact, a full 76% of the chatter was positive between December 1 and 15. Those who hadn’t specified their gender led the charge with 80% positive sentiment, women followed with 76% positive and men trailed with 65% positive. Men led in neutral/none (34%), largely reporting facts and sharing general information for which sentiment was neither positive or negative toward the movement. They also led in negative mentions (2%).

Sentiment: December 16-31

Sentiment began a bit of a transformation between December 16 and 31. Awareness of the movement was beginning to grow and the #IdleNoMore hashtag was gaining a significant amount of online traction. While overall sentiment remained 76% positive, neutral/none shrank from 24% earlier in the month to 9%, making way for an increase in negative sentiment from 1% to 6%. Noise makes its first appearance at 9% of overall traffic. Women led in positive tone (82%, up from 76% earlier in the month) while men led in negative (14%, up from 2%). Most of the noise emanated from the unspecified-gender group which led in that category (15%). The negative sentiment was made up mostly of ideological criticisms and complaints about train blockades.

Sentiment: January 1-19

Positive sentiment took a big hit in January, dropping from 76% to 50%. Women still led in that department (61%) with men (43%) trailing unspecified-gender (48%) by 5%. Negative sentiment more than doubled (from 6% to 15%) led by men (25%). The amount of criticism about blockades of train and travel routed increased, and expanded to include the audit results of Attawapiskat, apparent division between chiefs and between chiefs and the Idle No More movement, and the apparent indecision of Chief Theresa Spence. It was that confusion between Idle No More and First Nations’ leadership which caused the movement to try distancing itself. That helped contribute to criticism. Meanwhile, noise continued to increase (14% overall, up from 9%) led by the unspecified-gender group (24%).

Sentiment graphs

Because the attached image is small, let me clarify that the columns of graphs are, from left to right, overall sentiment, female sentiment, male sentiment and sentiment by those for whom gender cannot be immediately identified from the handle or actual name of their Twitter account. The rows, from top to bottom, are December 1-15, December 16-31 and January 1-19.

Note: The graphs in this post do not proportionally represent gender participation. Gender analysis is conducted in another post.


Index of my Idle No More at two months analysis series:

I conducted analysis using Marketwire/Sysomos Heartbeat and MAP, and a custom tool I’m having developed which I call Compass.

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